├── FashionMNIST.ipynb ├── HelloWorld.ipynb ├── Human_Or_Horse?.ipynb ├── Linear_Regression(NumPY,_Pandas_etc).ipynb ├── Machine Learning Roadmap for Absolute Beginners.pdf ├── MelbourneHousing.ipynb ├── Polynomial_Regression.ipynb ├── README.md └── Salary_Data.csv /FashionMNIST.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "FashionMNIST.ipynb", 7 | "provenance": [], 8 | "authorship_tag": "ABX9TyOqqd7sFeKg7l60H7uEHP74", 9 | "include_colab_link": true 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | } 15 | }, 16 | "cells": [ 17 | { 18 | "cell_type": "markdown", 19 | "metadata": { 20 | "id": "view-in-github", 21 | "colab_type": "text" 22 | }, 23 | "source": [ 24 | "\"Open" 25 | ] 26 | }, 27 | { 28 | "cell_type": "markdown", 29 | "metadata": { 30 | "id": "vFYH0Q02Tl5C", 31 | "colab_type": "text" 32 | }, 33 | "source": [ 34 | "Fashion MNIST is a library available on keras api" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "metadata": { 40 | "id": "f4tdFMNgThHz", 41 | "colab_type": "code", 42 | "colab": {} 43 | }, 44 | "source": [ 45 | "import tensorflow as tf\n", 46 | "from tensorflow import keras" 47 | ], 48 | "execution_count": 7, 49 | "outputs": [] 50 | }, 51 | { 52 | "cell_type": "code", 53 | "metadata": { 54 | "id": "iYdu0pkNd5b-", 55 | "colab_type": "code", 56 | "colab": {} 57 | }, 58 | "source": [ 59 | "class myCallback(tf.keras.callbacks.Callback):\n", 60 | " def on_epoch_end(self, epoch, logs={}):\n", 61 | " if(logs.get('loss')<0.4):\n", 62 | " print(\"\\nCancelling training because loss in low\")\n", 63 | " self.model.stop_training = True\n", 64 | "\n", 65 | "callbacks = myCallback()" 66 | ], 67 | "execution_count": 8, 68 | "outputs": [] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "metadata": { 73 | "id": "nyhmRtXgW19q", 74 | "colab_type": "code", 75 | "colab": { 76 | "base_uri": "https://localhost:8080/", 77 | "height": 768 78 | }, 79 | "outputId": "f7f7559e-4a05-41eb-e6c4-7f03c5553d46" 80 | }, 81 | "source": [ 82 | "fashion_mnist = keras.datasets.fashion_mnist\n", 83 | "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data() \n", 84 | "\n", 85 | "import numpy as np\n", 86 | "np.set_printoptions(linewidth=200)\n", 87 | "import matplotlib.pyplot as plt\n", 88 | "plt.imshow(train_images[0])\n", 89 | "print(train_labels[0])\n", 90 | "print(train_images[0])\n", 91 | "\n" 92 | ], 93 | "execution_count": 9, 94 | "outputs": [ 95 | { 96 | "output_type": "stream", 97 | "text": [ 98 | "9\n", 99 | "[[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", 100 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", 101 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n", 102 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 13 73 0 0 1 4 0 0 0 0 1 1 0]\n", 103 | " [ 0 0 0 0 0 0 0 0 0 0 0 0 3 0 36 136 127 62 54 0 0 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\n", 134 | "text/plain": [ 135 | "
" 136 | ] 137 | }, 138 | "metadata": { 139 | "tags": [], 140 | "needs_background": "light" 141 | } 142 | } 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "metadata": { 148 | "id": "v1jXAx-aXk6a", 149 | "colab_type": "code", 150 | "colab": {} 151 | }, 152 | "source": [ 153 | "#Normalize data\n", 154 | "train_images = train_images/255.0\n", 155 | "test_images = test_images/255.0" 156 | ], 157 | "execution_count": 10, 158 | "outputs": [] 159 | }, 160 | { 161 | "cell_type": "markdown", 162 | "metadata": { 163 | "id": "uLuA4mKNWn_L", 164 | "colab_type": "text" 165 | }, 166 | "source": [ 167 | "input size is 28 by 28 images. Middle layer is essentialy the gradient descnet function type. F(x) = x1 + x2 + x3 +... x128. F(x) returns numerical value from 1-10 which indicate what type of object this is. In fashion MNIST there are 10 classes. Thats why third layer has 10 for dimnesionality." 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "metadata": { 173 | "id": "6gmBaH_bXZVU", 174 | "colab_type": "code", 175 | "colab": {} 176 | }, 177 | "source": [ 178 | "model = keras.Sequential([keras.layers.Flatten(input_shape=(28,28)),\n", 179 | " keras.layers.Dense(123, activation=tf.nn.relu),\n", 180 | " keras.layers.Dense(10, activation=tf.nn.softmax)])" 181 | ], 182 | "execution_count": 11, 183 | "outputs": [] 184 | }, 185 | { 186 | "cell_type": "code", 187 | "metadata": { 188 | "id": "pwdUtVd3XaJ2", 189 | "colab_type": "code", 190 | "colab": { 191 | "base_uri": "https://localhost:8080/", 192 | "height": 139 193 | }, 194 | "outputId": "6c9f1f55-3ada-4212-fdb6-98ff7d730e83" 195 | }, 196 | "source": [ 197 | "model.compile(optimizer=tf.optimizers.Adam(),\n", 198 | " loss = 'sparse_categorical_crossentropy',\n", 199 | " metrics=['accuracy'])\n", 200 | "\n", 201 | "model.fit(train_images, train_labels, epochs=5, callbacks=[callbacks])" 202 | ], 203 | "execution_count": 12, 204 | "outputs": [ 205 | { 206 | "output_type": "stream", 207 | "text": [ 208 | "Epoch 1/5\n", 209 | "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4968 - accuracy: 0.8255\n", 210 | "Epoch 2/5\n", 211 | "1871/1875 [============================>.] - ETA: 0s - loss: 0.3728 - accuracy: 0.8650\n", 212 | "Cancelling training because loss in low\n", 213 | "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3730 - accuracy: 0.8649\n" 214 | ], 215 | "name": "stdout" 216 | }, 217 | { 218 | "output_type": "execute_result", 219 | "data": { 220 | "text/plain": [ 221 | "" 222 | ] 223 | }, 224 | "metadata": { 225 | "tags": [] 226 | }, 227 | "execution_count": 12 228 | } 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "metadata": { 234 | "id": "8qSWpLx_9Q0W", 235 | "colab_type": "code", 236 | "colab": { 237 | "base_uri": "https://localhost:8080/", 238 | "height": 52 239 | }, 240 | "outputId": "e1546757-7f01-44c4-bcce-6375e3172ece" 241 | }, 242 | "source": [ 243 | "model.evaluate(test_images, test_labels, batch_size=10000)" 244 | ], 245 | "execution_count": 13, 246 | "outputs": [ 247 | { 248 | "output_type": "stream", 249 | "text": [ 250 | "1/1 [==============================] - 0s 1ms/step - loss: 0.3804 - accuracy: 0.8634\n" 251 | ], 252 | "name": "stdout" 253 | }, 254 | { 255 | "output_type": "execute_result", 256 | "data": { 257 | "text/plain": [ 258 | "[0.3804126977920532, 0.8633999824523926]" 259 | ] 260 | }, 261 | "metadata": { 262 | "tags": [] 263 | }, 264 | "execution_count": 13 265 | } 266 | ] 267 | } 268 | ] 269 | } -------------------------------------------------------------------------------- /HelloWorld.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "HelloWorld.ipynb", 7 | "provenance": [], 8 | "collapsed_sections": [], 9 | "authorship_tag": "ABX9TyMFU4Hd7aBtAj8f6lWoghIL", 10 | "include_colab_link": true 11 | }, 12 | "kernelspec": { 13 | "name": "python3", 14 | "display_name": "Python 3" 15 | } 16 | }, 17 | "cells": [ 18 | { 19 | "cell_type": "markdown", 20 | "metadata": { 21 | "id": "view-in-github", 22 | "colab_type": "text" 23 | }, 24 | "source": [ 25 | "\"Open" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": { 31 | "id": "ouyFZFkiUJ_e", 32 | "colab_type": "text" 33 | }, 34 | "source": [ 35 | "Simple problem of finding a linear equation given x & y coordinated of points. First off import libraries" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "metadata": { 41 | "id": "zh5j4MrCU1fR", 42 | "colab_type": "code", 43 | "colab": {} 44 | }, 45 | "source": [ 46 | "import tensorflow as tf\n", 47 | "import numpy as np\n", 48 | "from tensorflow import keras" 49 | ], 50 | "execution_count": 2, 51 | "outputs": [] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "metadata": { 56 | "id": "Jv7ZdekmWRcH", 57 | "colab_type": "code", 58 | "colab": {} 59 | }, 60 | "source": [ 61 | "model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])" 62 | ], 63 | "execution_count": 14, 64 | "outputs": [] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "metadata": { 69 | "colab_type": "code", 70 | "id": "m8YQN1H41L-Y", 71 | "colab": {} 72 | }, 73 | "source": [ 74 | "model.compile(optimizer=keras.optimizers.SGD(lr=0.01, nesterov=True), loss='mean_squared_error')\n" 75 | ], 76 | "execution_count": 19, 77 | "outputs": [] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "metadata": { 82 | "id": "f3ODtKOlY5SC", 83 | "colab_type": "code", 84 | "colab": {} 85 | }, 86 | "source": [ 87 | "xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)\n", 88 | "ys = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype=float)" 89 | ], 90 | "execution_count": 20, 91 | "outputs": [] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "metadata": { 96 | "colab_type": "code", 97 | "id": "lpRrl7WK10Pq", 98 | "colab": { 99 | "base_uri": "https://localhost:8080/", 100 | "height": 1000 101 | }, 102 | "outputId": "bbd5217f-cf6f-4ad1-cac2-88126e240129" 103 | }, 104 | "source": [ 105 | "model.fit(xs, ys, epochs=500)" 106 | ], 107 | "execution_count": 21, 108 | "outputs": [ 109 | { 110 | "output_type": "stream", 111 | "text": [ 112 | "Epoch 1/500\n", 113 | "1/1 [==============================] - 0s 1ms/step - loss: 11.5061\n", 114 | "Epoch 2/500\n", 115 | "1/1 [==============================] - 0s 1ms/step - loss: 9.4226\n", 116 | "Epoch 3/500\n", 117 | "1/1 [==============================] - 0s 1ms/step - loss: 7.7757\n", 118 | "Epoch 4/500\n", 119 | "1/1 [==============================] - 0s 3ms/step - loss: 6.4726\n", 120 | "Epoch 5/500\n", 121 | "1/1 [==============================] - 0s 3ms/step - loss: 5.4401\n", 122 | "Epoch 6/500\n", 123 | "1/1 [==============================] - 0s 2ms/step - loss: 4.6206\n", 124 | "Epoch 7/500\n", 125 | "1/1 [==============================] - 0s 2ms/step - loss: 3.9689\n", 126 | "Epoch 8/500\n", 127 | "1/1 [==============================] - 0s 2ms/step - loss: 3.4493\n", 128 | "Epoch 9/500\n", 129 | "1/1 [==============================] - 0s 2ms/step - loss: 3.0338\n", 130 | "Epoch 10/500\n", 131 | "1/1 [==============================] - 0s 2ms/step - loss: 2.7003\n", 132 | "Epoch 11/500\n", 133 | "1/1 [==============================] - 0s 2ms/step - loss: 2.4315\n", 134 | "Epoch 12/500\n", 135 | "1/1 [==============================] - 0s 2ms/step - loss: 2.2137\n", 136 | "Epoch 13/500\n", 137 | "1/1 [==============================] - 0s 2ms/step - loss: 2.0362\n", 138 | "Epoch 14/500\n", 139 | "1/1 [==============================] - 0s 2ms/step - loss: 1.8905\n", 140 | "Epoch 15/500\n", 141 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7699\n", 142 | "Epoch 16/500\n", 143 | "1/1 [==============================] - 0s 2ms/step - loss: 1.6692\n", 144 | "Epoch 17/500\n", 145 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5844\n", 146 | "Epoch 18/500\n", 147 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5120\n", 148 | "Epoch 19/500\n", 149 | "1/1 [==============================] - 0s 2ms/step - loss: 1.4496\n", 150 | "Epoch 20/500\n", 151 | "1/1 [==============================] - 0s 3ms/step - loss: 1.3952\n", 152 | "Epoch 21/500\n", 153 | "1/1 [==============================] - 0s 2ms/step - loss: 1.3472\n", 154 | "Epoch 22/500\n", 155 | "1/1 [==============================] - 0s 2ms/step - loss: 1.3043\n", 156 | "Epoch 23/500\n", 157 | "1/1 [==============================] - 0s 2ms/step - loss: 1.2655\n", 158 | "Epoch 24/500\n", 159 | "1/1 [==============================] - 0s 2ms/step - loss: 1.2300\n", 160 | "Epoch 25/500\n", 161 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1974\n", 162 | "Epoch 26/500\n", 163 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1669\n", 164 | "Epoch 27/500\n", 165 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1383\n", 166 | "Epoch 28/500\n", 167 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1113\n", 168 | "Epoch 29/500\n", 169 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0857\n", 170 | "Epoch 30/500\n", 171 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0611\n", 172 | "Epoch 31/500\n", 173 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0376\n", 174 | "Epoch 32/500\n", 175 | "1/1 [==============================] - 0s 3ms/step - loss: 1.0149\n", 176 | "Epoch 33/500\n", 177 | "1/1 [==============================] - 0s 3ms/step - loss: 0.9929\n", 178 | "Epoch 34/500\n", 179 | "1/1 [==============================] - 0s 3ms/step - loss: 0.9717\n", 180 | "Epoch 35/500\n", 181 | "1/1 [==============================] - 0s 2ms/step - loss: 0.9511\n", 182 | "Epoch 36/500\n", 183 | "1/1 [==============================] - 0s 3ms/step - loss: 0.9310\n", 184 | "Epoch 37/500\n", 185 | "1/1 [==============================] - 0s 9ms/step - loss: 0.9115\n", 186 | "Epoch 38/500\n", 187 | "1/1 [==============================] - 0s 2ms/step - loss: 0.8924\n", 188 | "Epoch 39/500\n", 189 | "1/1 [==============================] - 0s 3ms/step - loss: 0.8738\n", 190 | "Epoch 40/500\n", 191 | "1/1 [==============================] - 0s 2ms/step - loss: 0.8557\n", 192 | "Epoch 41/500\n", 193 | "1/1 [==============================] - 0s 2ms/step - loss: 0.8379\n", 194 | "Epoch 42/500\n", 195 | "1/1 [==============================] - 0s 2ms/step - loss: 0.8206\n", 196 | "Epoch 43/500\n", 197 | "1/1 [==============================] - 0s 2ms/step - loss: 0.8036\n", 198 | "Epoch 44/500\n", 199 | "1/1 [==============================] - 0s 2ms/step - loss: 0.7870\n", 200 | "Epoch 45/500\n", 201 | "1/1 [==============================] - 0s 2ms/step - loss: 0.7708\n", 202 | "Epoch 46/500\n", 203 | "1/1 [==============================] - 0s 2ms/step - loss: 0.7549\n", 204 | "Epoch 47/500\n", 205 | "1/1 [==============================] - 0s 2ms/step - loss: 0.7394\n", 206 | "Epoch 48/500\n", 207 | "1/1 [==============================] - 0s 2ms/step - loss: 0.7242\n", 208 | "Epoch 49/500\n", 209 | "1/1 [==============================] - 0s 2ms/step - loss: 0.7093\n", 210 | "Epoch 50/500\n", 211 | "1/1 [==============================] - 0s 2ms/step - loss: 0.6947\n", 212 | "Epoch 51/500\n", 213 | "1/1 [==============================] - 0s 3ms/step - loss: 0.6804\n", 214 | "Epoch 52/500\n", 215 | "1/1 [==============================] - 0s 2ms/step - loss: 0.6664\n", 216 | "Epoch 53/500\n", 217 | "1/1 [==============================] - 0s 3ms/step - loss: 0.6527\n", 218 | "Epoch 54/500\n", 219 | "1/1 [==============================] - 0s 3ms/step - loss: 0.6393\n", 220 | "Epoch 55/500\n", 221 | "1/1 [==============================] - 0s 2ms/step - loss: 0.6262\n", 222 | "Epoch 56/500\n", 223 | "1/1 [==============================] - 0s 2ms/step - loss: 0.6133\n", 224 | "Epoch 57/500\n", 225 | "1/1 [==============================] - 0s 4ms/step - loss: 0.6007\n", 226 | "Epoch 58/500\n", 227 | "1/1 [==============================] - 0s 3ms/step - loss: 0.5884\n", 228 | "Epoch 59/500\n", 229 | "1/1 [==============================] - 0s 3ms/step - loss: 0.5763\n", 230 | "Epoch 60/500\n", 231 | "1/1 [==============================] - 0s 3ms/step - loss: 0.5644\n", 232 | "Epoch 61/500\n", 233 | "1/1 [==============================] - 0s 2ms/step - loss: 0.5528\n", 234 | "Epoch 62/500\n", 235 | "1/1 [==============================] - 0s 2ms/step - loss: 0.5415\n", 236 | "Epoch 63/500\n", 237 | "1/1 [==============================] - 0s 2ms/step - loss: 0.5304\n", 238 | "Epoch 64/500\n", 239 | "1/1 [==============================] - 0s 3ms/step - loss: 0.5195\n", 240 | "Epoch 65/500\n", 241 | "1/1 [==============================] - 0s 3ms/step - loss: 0.5088\n", 242 | "Epoch 66/500\n", 243 | "1/1 [==============================] - 0s 2ms/step - loss: 0.4983\n", 244 | "Epoch 67/500\n", 245 | "1/1 [==============================] - 0s 3ms/step - loss: 0.4881\n", 246 | "Epoch 68/500\n", 247 | "1/1 [==============================] - 0s 3ms/step - loss: 0.4781\n", 248 | "Epoch 69/500\n", 249 | "1/1 [==============================] - 0s 2ms/step - loss: 0.4683\n", 250 | "Epoch 70/500\n", 251 | "1/1 [==============================] - 0s 2ms/step - loss: 0.4586\n", 252 | "Epoch 71/500\n", 253 | "1/1 [==============================] - 0s 3ms/step - loss: 0.4492\n", 254 | "Epoch 72/500\n", 255 | "1/1 [==============================] - 0s 2ms/step - loss: 0.4400\n", 256 | "Epoch 73/500\n", 257 | "1/1 [==============================] - 0s 2ms/step - loss: 0.4310\n", 258 | "Epoch 74/500\n", 259 | "1/1 [==============================] - 0s 2ms/step - loss: 0.4221\n", 260 | "Epoch 75/500\n", 261 | "1/1 [==============================] - 0s 2ms/step - loss: 0.4134\n", 262 | "Epoch 76/500\n", 263 | "1/1 [==============================] - 0s 2ms/step - loss: 0.4049\n", 264 | "Epoch 77/500\n", 265 | "1/1 [==============================] - 0s 2ms/step - loss: 0.3966\n", 266 | "Epoch 78/500\n", 267 | "1/1 [==============================] - 0s 2ms/step - loss: 0.3885\n", 268 | "Epoch 79/500\n", 269 | "1/1 [==============================] - 0s 1ms/step - loss: 0.3805\n", 270 | "Epoch 80/500\n", 271 | "1/1 [==============================] - 0s 2ms/step - loss: 0.3727\n", 272 | "Epoch 81/500\n", 273 | "1/1 [==============================] - 0s 3ms/step - loss: 0.3650\n", 274 | "Epoch 82/500\n", 275 | "1/1 [==============================] - 0s 2ms/step - loss: 0.3575\n", 276 | "Epoch 83/500\n", 277 | "1/1 [==============================] - 0s 1ms/step - loss: 0.3502\n", 278 | "Epoch 84/500\n", 279 | "1/1 [==============================] - 0s 2ms/step - loss: 0.3430\n", 280 | "Epoch 85/500\n", 281 | "1/1 [==============================] - 0s 3ms/step - loss: 0.3359\n", 282 | "Epoch 86/500\n", 283 | "1/1 [==============================] - 0s 2ms/step - loss: 0.3290\n", 284 | "Epoch 87/500\n", 285 | "1/1 [==============================] - 0s 2ms/step - loss: 0.3223\n", 286 | "Epoch 88/500\n", 287 | "1/1 [==============================] - 0s 1ms/step - loss: 0.3157\n", 288 | "Epoch 89/500\n", 289 | "1/1 [==============================] - 0s 2ms/step - loss: 0.3092\n", 290 | "Epoch 90/500\n", 291 | "1/1 [==============================] - 0s 2ms/step - loss: 0.3028\n", 292 | "Epoch 91/500\n", 293 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2966\n", 294 | "Epoch 92/500\n", 295 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2905\n", 296 | "Epoch 93/500\n", 297 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2846\n", 298 | "Epoch 94/500\n", 299 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2787\n", 300 | "Epoch 95/500\n", 301 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2730\n", 302 | "Epoch 96/500\n", 303 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2674\n", 304 | "Epoch 97/500\n", 305 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2619\n", 306 | "Epoch 98/500\n", 307 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2565\n", 308 | "Epoch 99/500\n", 309 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2512\n", 310 | "Epoch 100/500\n", 311 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2461\n", 312 | "Epoch 101/500\n", 313 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2410\n", 314 | "Epoch 102/500\n", 315 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2361\n", 316 | "Epoch 103/500\n", 317 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2312\n", 318 | "Epoch 104/500\n", 319 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2265\n", 320 | "Epoch 105/500\n", 321 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2218\n", 322 | "Epoch 106/500\n", 323 | "1/1 [==============================] - 0s 1ms/step - loss: 0.2173\n", 324 | "Epoch 107/500\n", 325 | "1/1 [==============================] - 0s 3ms/step - loss: 0.2128\n", 326 | "Epoch 108/500\n", 327 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2084\n", 328 | "Epoch 109/500\n", 329 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2041\n", 330 | "Epoch 110/500\n", 331 | "1/1 [==============================] - 0s 2ms/step - loss: 0.2000\n", 332 | "Epoch 111/500\n", 333 | "1/1 [==============================] - 0s 1ms/step - loss: 0.1958\n", 334 | "Epoch 112/500\n", 335 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1918\n", 336 | "Epoch 113/500\n", 337 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1879\n", 338 | "Epoch 114/500\n", 339 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1840\n", 340 | "Epoch 115/500\n", 341 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1802\n", 342 | "Epoch 116/500\n", 343 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1765\n", 344 | "Epoch 117/500\n", 345 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1729\n", 346 | "Epoch 118/500\n", 347 | "1/1 [==============================] - 0s 1ms/step - loss: 0.1694\n", 348 | "Epoch 119/500\n", 349 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1659\n", 350 | "Epoch 120/500\n", 351 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1625\n", 352 | "Epoch 121/500\n", 353 | "1/1 [==============================] - 0s 1ms/step - loss: 0.1591\n", 354 | "Epoch 122/500\n", 355 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1559\n", 356 | "Epoch 123/500\n", 357 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1527\n", 358 | "Epoch 124/500\n", 359 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1495\n", 360 | "Epoch 125/500\n", 361 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1465\n", 362 | "Epoch 126/500\n", 363 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1435\n", 364 | "Epoch 127/500\n", 365 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1405\n", 366 | "Epoch 128/500\n", 367 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1376\n", 368 | "Epoch 129/500\n", 369 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1348\n", 370 | "Epoch 130/500\n", 371 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1320\n", 372 | "Epoch 131/500\n", 373 | "1/1 [==============================] - 0s 1ms/step - loss: 0.1293\n", 374 | "Epoch 132/500\n", 375 | "1/1 [==============================] - 0s 1ms/step - loss: 0.1267\n", 376 | "Epoch 133/500\n", 377 | "1/1 [==============================] - 0s 1ms/step - loss: 0.1241\n", 378 | "Epoch 134/500\n", 379 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1215\n", 380 | "Epoch 135/500\n", 381 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1190\n", 382 | "Epoch 136/500\n", 383 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1166\n", 384 | "Epoch 137/500\n", 385 | "1/1 [==============================] - 0s 1ms/step - loss: 0.1142\n", 386 | "Epoch 138/500\n", 387 | "1/1 [==============================] - 0s 1ms/step - loss: 0.1118\n", 388 | "Epoch 139/500\n", 389 | "1/1 [==============================] - 0s 1ms/step - loss: 0.1095\n", 390 | "Epoch 140/500\n", 391 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1073\n", 392 | "Epoch 141/500\n", 393 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1051\n", 394 | "Epoch 142/500\n", 395 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1029\n", 396 | "Epoch 143/500\n", 397 | "1/1 [==============================] - 0s 2ms/step - loss: 0.1008\n", 398 | "Epoch 144/500\n", 399 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0987\n", 400 | "Epoch 145/500\n", 401 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0967\n", 402 | "Epoch 146/500\n", 403 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0947\n", 404 | "Epoch 147/500\n", 405 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0928\n", 406 | "Epoch 148/500\n", 407 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0909\n", 408 | "Epoch 149/500\n", 409 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0890\n", 410 | "Epoch 150/500\n", 411 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0872\n", 412 | "Epoch 151/500\n", 413 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0854\n", 414 | "Epoch 152/500\n", 415 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0836\n", 416 | "Epoch 153/500\n", 417 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0819\n", 418 | "Epoch 154/500\n", 419 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0802\n", 420 | "Epoch 155/500\n", 421 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0786\n", 422 | "Epoch 156/500\n", 423 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0770\n", 424 | "Epoch 157/500\n", 425 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0754\n", 426 | "Epoch 158/500\n", 427 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0738\n", 428 | "Epoch 159/500\n", 429 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0723\n", 430 | "Epoch 160/500\n", 431 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0708\n", 432 | "Epoch 161/500\n", 433 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0694\n", 434 | "Epoch 162/500\n", 435 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0680\n", 436 | "Epoch 163/500\n", 437 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0666\n", 438 | "Epoch 164/500\n", 439 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0652\n", 440 | "Epoch 165/500\n", 441 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0639\n", 442 | "Epoch 166/500\n", 443 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0625\n", 444 | "Epoch 167/500\n", 445 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0613\n", 446 | "Epoch 168/500\n", 447 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0600\n", 448 | "Epoch 169/500\n", 449 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0588\n", 450 | "Epoch 170/500\n", 451 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0576\n", 452 | "Epoch 171/500\n", 453 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0564\n", 454 | "Epoch 172/500\n", 455 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0552\n", 456 | "Epoch 173/500\n", 457 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0541\n", 458 | "Epoch 174/500\n", 459 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0530\n", 460 | "Epoch 175/500\n", 461 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0519\n", 462 | "Epoch 176/500\n", 463 | "1/1 [==============================] - 0s 5ms/step - loss: 0.0508\n", 464 | "Epoch 177/500\n", 465 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0498\n", 466 | "Epoch 178/500\n", 467 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0488\n", 468 | "Epoch 179/500\n", 469 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0478\n", 470 | "Epoch 180/500\n", 471 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0468\n", 472 | "Epoch 181/500\n", 473 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0458\n", 474 | "Epoch 182/500\n", 475 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0449\n", 476 | "Epoch 183/500\n", 477 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0439\n", 478 | "Epoch 184/500\n", 479 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0430\n", 480 | "Epoch 185/500\n", 481 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0422\n", 482 | "Epoch 186/500\n", 483 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0413\n", 484 | "Epoch 187/500\n", 485 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0404\n", 486 | "Epoch 188/500\n", 487 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0396\n", 488 | "Epoch 189/500\n", 489 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0388\n", 490 | "Epoch 190/500\n", 491 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0380\n", 492 | "Epoch 191/500\n", 493 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0372\n", 494 | "Epoch 192/500\n", 495 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0365\n", 496 | "Epoch 193/500\n", 497 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0357\n", 498 | "Epoch 194/500\n", 499 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0350\n", 500 | "Epoch 195/500\n", 501 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0343\n", 502 | "Epoch 196/500\n", 503 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0336\n", 504 | "Epoch 197/500\n", 505 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0329\n", 506 | "Epoch 198/500\n", 507 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0322\n", 508 | "Epoch 199/500\n", 509 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0315\n", 510 | "Epoch 200/500\n", 511 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0309\n", 512 | "Epoch 201/500\n", 513 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0302\n", 514 | "Epoch 202/500\n", 515 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0296\n", 516 | "Epoch 203/500\n", 517 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0290\n", 518 | "Epoch 204/500\n", 519 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0284\n", 520 | "Epoch 205/500\n", 521 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0278\n", 522 | "Epoch 206/500\n", 523 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0273\n", 524 | "Epoch 207/500\n", 525 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0267\n", 526 | "Epoch 208/500\n", 527 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0262\n", 528 | "Epoch 209/500\n", 529 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0256\n", 530 | "Epoch 210/500\n", 531 | "1/1 [==============================] - 0s 4ms/step - loss: 0.0251\n", 532 | "Epoch 211/500\n", 533 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0246\n", 534 | "Epoch 212/500\n", 535 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0241\n", 536 | "Epoch 213/500\n", 537 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0236\n", 538 | "Epoch 214/500\n", 539 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0231\n", 540 | "Epoch 215/500\n", 541 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0226\n", 542 | "Epoch 216/500\n", 543 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0222\n", 544 | "Epoch 217/500\n", 545 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0217\n", 546 | "Epoch 218/500\n", 547 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0213\n", 548 | "Epoch 219/500\n", 549 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0208\n", 550 | "Epoch 220/500\n", 551 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0204\n", 552 | "Epoch 221/500\n", 553 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0200\n", 554 | "Epoch 222/500\n", 555 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0196\n", 556 | "Epoch 223/500\n", 557 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0192\n", 558 | "Epoch 224/500\n", 559 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0188\n", 560 | "Epoch 225/500\n", 561 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0184\n", 562 | "Epoch 226/500\n", 563 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0180\n", 564 | "Epoch 227/500\n", 565 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0176\n", 566 | "Epoch 228/500\n", 567 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0173\n", 568 | "Epoch 229/500\n", 569 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0169\n", 570 | "Epoch 230/500\n", 571 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0166\n", 572 | "Epoch 231/500\n", 573 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0162\n", 574 | "Epoch 232/500\n", 575 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0159\n", 576 | "Epoch 233/500\n", 577 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0156\n", 578 | "Epoch 234/500\n", 579 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0152\n", 580 | "Epoch 235/500\n", 581 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0149\n", 582 | "Epoch 236/500\n", 583 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0146\n", 584 | "Epoch 237/500\n", 585 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0143\n", 586 | "Epoch 238/500\n", 587 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0140\n", 588 | "Epoch 239/500\n", 589 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0137\n", 590 | "Epoch 240/500\n", 591 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0135\n", 592 | "Epoch 241/500\n", 593 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0132\n", 594 | "Epoch 242/500\n", 595 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0129\n", 596 | "Epoch 243/500\n", 597 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0127\n", 598 | "Epoch 244/500\n", 599 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0124\n", 600 | "Epoch 245/500\n", 601 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0121\n", 602 | "Epoch 246/500\n", 603 | "1/1 [==============================] - 0s 4ms/step - loss: 0.0119\n", 604 | "Epoch 247/500\n", 605 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0116\n", 606 | "Epoch 248/500\n", 607 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0114\n", 608 | "Epoch 249/500\n", 609 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0112\n", 610 | "Epoch 250/500\n", 611 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0109\n", 612 | "Epoch 251/500\n", 613 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0107\n", 614 | "Epoch 252/500\n", 615 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0105\n", 616 | "Epoch 253/500\n", 617 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0103\n", 618 | "Epoch 254/500\n", 619 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0101\n", 620 | "Epoch 255/500\n", 621 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0099\n", 622 | "Epoch 256/500\n", 623 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0097\n", 624 | "Epoch 257/500\n", 625 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0095\n", 626 | "Epoch 258/500\n", 627 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0093\n", 628 | "Epoch 259/500\n", 629 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0091\n", 630 | "Epoch 260/500\n", 631 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0089\n", 632 | "Epoch 261/500\n", 633 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0087\n", 634 | "Epoch 262/500\n", 635 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0085\n", 636 | "Epoch 263/500\n", 637 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0084\n", 638 | "Epoch 264/500\n", 639 | "1/1 [==============================] - 0s 3ms/step - loss: 0.0082\n", 640 | "Epoch 265/500\n", 641 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0080\n", 642 | "Epoch 266/500\n", 643 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0078\n", 644 | "Epoch 267/500\n", 645 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0077\n", 646 | "Epoch 268/500\n", 647 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0075\n", 648 | "Epoch 269/500\n", 649 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0074\n", 650 | "Epoch 270/500\n", 651 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0072\n", 652 | "Epoch 271/500\n", 653 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0071\n", 654 | "Epoch 272/500\n", 655 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0069\n", 656 | "Epoch 273/500\n", 657 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0068\n", 658 | "Epoch 274/500\n", 659 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0066\n", 660 | "Epoch 275/500\n", 661 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0065\n", 662 | "Epoch 276/500\n", 663 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0064\n", 664 | "Epoch 277/500\n", 665 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0062\n", 666 | "Epoch 278/500\n", 667 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0061\n", 668 | "Epoch 279/500\n", 669 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0060\n", 670 | "Epoch 280/500\n", 671 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0059\n", 672 | "Epoch 281/500\n", 673 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0057\n", 674 | "Epoch 282/500\n", 675 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0056\n", 676 | "Epoch 283/500\n", 677 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0055\n", 678 | "Epoch 284/500\n", 679 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0054\n", 680 | "Epoch 285/500\n", 681 | "1/1 [==============================] - 0s 3ms/step - loss: 0.0053\n", 682 | "Epoch 286/500\n", 683 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0052\n", 684 | "Epoch 287/500\n", 685 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0051\n", 686 | "Epoch 288/500\n", 687 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0050\n", 688 | "Epoch 289/500\n", 689 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0049\n", 690 | "Epoch 290/500\n", 691 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0048\n", 692 | "Epoch 291/500\n", 693 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0047\n", 694 | "Epoch 292/500\n", 695 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0046\n", 696 | "Epoch 293/500\n", 697 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0045\n", 698 | "Epoch 294/500\n", 699 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0044\n", 700 | "Epoch 295/500\n", 701 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0043\n", 702 | "Epoch 296/500\n", 703 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0042\n", 704 | "Epoch 297/500\n", 705 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0041\n", 706 | "Epoch 298/500\n", 707 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0040\n", 708 | "Epoch 299/500\n", 709 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0040\n", 710 | "Epoch 300/500\n", 711 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0039\n", 712 | "Epoch 301/500\n", 713 | "1/1 [==============================] - 0s 4ms/step - loss: 0.0038\n", 714 | "Epoch 302/500\n", 715 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0037\n", 716 | "Epoch 303/500\n", 717 | "1/1 [==============================] - 0s 4ms/step - loss: 0.0036\n", 718 | "Epoch 304/500\n", 719 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0036\n", 720 | "Epoch 305/500\n", 721 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0035\n", 722 | "Epoch 306/500\n", 723 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0034\n", 724 | "Epoch 307/500\n", 725 | "1/1 [==============================] - 0s 881us/step - loss: 0.0034\n", 726 | "Epoch 308/500\n", 727 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0033\n", 728 | "Epoch 309/500\n", 729 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0032\n", 730 | "Epoch 310/500\n", 731 | "1/1 [==============================] - 0s 920us/step - loss: 0.0031\n", 732 | "Epoch 311/500\n", 733 | "1/1 [==============================] - 0s 3ms/step - loss: 0.0031\n", 734 | "Epoch 312/500\n", 735 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0030\n", 736 | "Epoch 313/500\n", 737 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0030\n", 738 | "Epoch 314/500\n", 739 | "1/1 [==============================] - 0s 969us/step - loss: 0.0029\n", 740 | "Epoch 315/500\n", 741 | "1/1 [==============================] - 0s 911us/step - loss: 0.0028\n", 742 | "Epoch 316/500\n", 743 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0028\n", 744 | "Epoch 317/500\n", 745 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0027\n", 746 | "Epoch 318/500\n", 747 | "1/1 [==============================] - 0s 925us/step - loss: 0.0027\n", 748 | "Epoch 319/500\n", 749 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0026\n", 750 | "Epoch 320/500\n", 751 | "1/1 [==============================] - 0s 952us/step - loss: 0.0026\n", 752 | "Epoch 321/500\n", 753 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0025\n", 754 | "Epoch 322/500\n", 755 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0025\n", 756 | "Epoch 323/500\n", 757 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0024\n", 758 | "Epoch 324/500\n", 759 | "1/1 [==============================] - 0s 987us/step - loss: 0.0024\n", 760 | "Epoch 325/500\n", 761 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0023\n", 762 | "Epoch 326/500\n", 763 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0023\n", 764 | "Epoch 327/500\n", 765 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0022\n", 766 | "Epoch 328/500\n", 767 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0022\n", 768 | "Epoch 329/500\n", 769 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n", 770 | "Epoch 330/500\n", 771 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0021\n", 772 | "Epoch 331/500\n", 773 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0020\n", 774 | "Epoch 332/500\n", 775 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0020\n", 776 | "Epoch 333/500\n", 777 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0020\n", 778 | "Epoch 334/500\n", 779 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0019\n", 780 | "Epoch 335/500\n", 781 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0019\n", 782 | "Epoch 336/500\n", 783 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0018\n", 784 | "Epoch 337/500\n", 785 | "1/1 [==============================] - 0s 3ms/step - loss: 0.0018\n", 786 | "Epoch 338/500\n", 787 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0018\n", 788 | "Epoch 339/500\n", 789 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0017\n", 790 | "Epoch 340/500\n", 791 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0017\n", 792 | "Epoch 341/500\n", 793 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0017\n", 794 | "Epoch 342/500\n", 795 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0016\n", 796 | "Epoch 343/500\n", 797 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0016\n", 798 | "Epoch 344/500\n", 799 | "1/1 [==============================] - 0s 5ms/step - loss: 0.0016\n", 800 | "Epoch 345/500\n", 801 | "1/1 [==============================] - 0s 997us/step - loss: 0.0015\n", 802 | "Epoch 346/500\n", 803 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0015\n", 804 | "Epoch 347/500\n", 805 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0015\n", 806 | "Epoch 348/500\n", 807 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0014\n", 808 | "Epoch 349/500\n", 809 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0014\n", 810 | "Epoch 350/500\n", 811 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0014\n", 812 | "Epoch 351/500\n", 813 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0013\n", 814 | "Epoch 352/500\n", 815 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0013\n", 816 | "Epoch 353/500\n", 817 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0013\n", 818 | "Epoch 354/500\n", 819 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0013\n", 820 | "Epoch 355/500\n", 821 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0012\n", 822 | "Epoch 356/500\n", 823 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0012\n", 824 | "Epoch 357/500\n", 825 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0012\n", 826 | "Epoch 358/500\n", 827 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0012\n", 828 | "Epoch 359/500\n", 829 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0011\n", 830 | "Epoch 360/500\n", 831 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0011\n", 832 | "Epoch 361/500\n", 833 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0011\n", 834 | "Epoch 362/500\n", 835 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0011\n", 836 | "Epoch 363/500\n", 837 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0010\n", 838 | "Epoch 364/500\n", 839 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0010\n", 840 | "Epoch 365/500\n", 841 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0010\n", 842 | "Epoch 366/500\n", 843 | "1/1 [==============================] - 0s 1ms/step - loss: 9.8504e-04\n", 844 | "Epoch 367/500\n", 845 | "1/1 [==============================] - 0s 3ms/step - loss: 9.6481e-04\n", 846 | "Epoch 368/500\n", 847 | "1/1 [==============================] - 0s 2ms/step - loss: 9.4499e-04\n", 848 | "Epoch 369/500\n", 849 | "1/1 [==============================] - 0s 4ms/step - loss: 9.2558e-04\n", 850 | "Epoch 370/500\n", 851 | "1/1 [==============================] - 0s 2ms/step - loss: 9.0657e-04\n", 852 | "Epoch 371/500\n", 853 | "1/1 [==============================] - 0s 1ms/step - loss: 8.8795e-04\n", 854 | "Epoch 372/500\n", 855 | "1/1 [==============================] - 0s 2ms/step - loss: 8.6971e-04\n", 856 | "Epoch 373/500\n", 857 | "1/1 [==============================] - 0s 1ms/step - loss: 8.5184e-04\n", 858 | "Epoch 374/500\n", 859 | "1/1 [==============================] - 0s 2ms/step - loss: 8.3435e-04\n", 860 | "Epoch 375/500\n", 861 | "1/1 [==============================] - 0s 2ms/step - loss: 8.1721e-04\n", 862 | "Epoch 376/500\n", 863 | "1/1 [==============================] - 0s 2ms/step - loss: 8.0042e-04\n", 864 | "Epoch 377/500\n", 865 | "1/1 [==============================] - 0s 2ms/step - loss: 7.8398e-04\n", 866 | "Epoch 378/500\n", 867 | "1/1 [==============================] - 0s 1ms/step - loss: 7.6788e-04\n", 868 | "Epoch 379/500\n", 869 | "1/1 [==============================] - 0s 2ms/step - loss: 7.5211e-04\n", 870 | "Epoch 380/500\n", 871 | "1/1 [==============================] - 0s 1ms/step - loss: 7.3666e-04\n", 872 | "Epoch 381/500\n", 873 | "1/1 [==============================] - 0s 2ms/step - loss: 7.2152e-04\n", 874 | "Epoch 382/500\n", 875 | "1/1 [==============================] - 0s 2ms/step - loss: 7.0671e-04\n", 876 | "Epoch 383/500\n", 877 | "1/1 [==============================] - 0s 1ms/step - loss: 6.9219e-04\n", 878 | "Epoch 384/500\n", 879 | "1/1 [==============================] - 0s 3ms/step - loss: 6.7797e-04\n", 880 | "Epoch 385/500\n", 881 | "1/1 [==============================] - 0s 1ms/step - loss: 6.6405e-04\n", 882 | "Epoch 386/500\n", 883 | "1/1 [==============================] - 0s 3ms/step - loss: 6.5040e-04\n", 884 | "Epoch 387/500\n", 885 | "1/1 [==============================] - 0s 2ms/step - loss: 6.3705e-04\n", 886 | "Epoch 388/500\n", 887 | "1/1 [==============================] - 0s 3ms/step - loss: 6.2396e-04\n", 888 | "Epoch 389/500\n", 889 | "1/1 [==============================] - 0s 2ms/step - loss: 6.1114e-04\n", 890 | "Epoch 390/500\n", 891 | "1/1 [==============================] - 0s 3ms/step - loss: 5.9859e-04\n", 892 | "Epoch 391/500\n", 893 | "1/1 [==============================] - 0s 3ms/step - loss: 5.8630e-04\n", 894 | "Epoch 392/500\n", 895 | "1/1 [==============================] - 0s 2ms/step - loss: 5.7425e-04\n", 896 | "Epoch 393/500\n", 897 | "1/1 [==============================] - 0s 2ms/step - loss: 5.6246e-04\n", 898 | "Epoch 394/500\n", 899 | "1/1 [==============================] - 0s 3ms/step - loss: 5.5091e-04\n", 900 | "Epoch 395/500\n", 901 | "1/1 [==============================] - 0s 2ms/step - loss: 5.3959e-04\n", 902 | "Epoch 396/500\n", 903 | "1/1 [==============================] - 0s 2ms/step - loss: 5.2851e-04\n", 904 | "Epoch 397/500\n", 905 | "1/1 [==============================] - 0s 2ms/step - loss: 5.1765e-04\n", 906 | "Epoch 398/500\n", 907 | "1/1 [==============================] - 0s 2ms/step - loss: 5.0702e-04\n", 908 | "Epoch 399/500\n", 909 | "1/1 [==============================] - 0s 2ms/step - loss: 4.9660e-04\n", 910 | "Epoch 400/500\n", 911 | "1/1 [==============================] - 0s 3ms/step - loss: 4.8641e-04\n", 912 | "Epoch 401/500\n", 913 | "1/1 [==============================] - 0s 3ms/step - loss: 4.7641e-04\n", 914 | "Epoch 402/500\n", 915 | "1/1 [==============================] - 0s 2ms/step - loss: 4.6663e-04\n", 916 | "Epoch 403/500\n", 917 | "1/1 [==============================] - 0s 2ms/step - loss: 4.5704e-04\n", 918 | "Epoch 404/500\n", 919 | "1/1 [==============================] - 0s 2ms/step - loss: 4.4766e-04\n", 920 | "Epoch 405/500\n", 921 | "1/1 [==============================] - 0s 2ms/step - loss: 4.3846e-04\n", 922 | "Epoch 406/500\n", 923 | "1/1 [==============================] - 0s 2ms/step - loss: 4.2945e-04\n", 924 | "Epoch 407/500\n", 925 | "1/1 [==============================] - 0s 2ms/step - loss: 4.2063e-04\n", 926 | "Epoch 408/500\n", 927 | "1/1 [==============================] - 0s 2ms/step - loss: 4.1199e-04\n", 928 | "Epoch 409/500\n", 929 | "1/1 [==============================] - 0s 2ms/step - loss: 4.0353e-04\n", 930 | "Epoch 410/500\n", 931 | "1/1 [==============================] - 0s 1ms/step - loss: 3.9524e-04\n", 932 | "Epoch 411/500\n", 933 | "1/1 [==============================] - 0s 1ms/step - loss: 3.8712e-04\n", 934 | "Epoch 412/500\n", 935 | "1/1 [==============================] - 0s 2ms/step - loss: 3.7917e-04\n", 936 | "Epoch 413/500\n", 937 | "1/1 [==============================] - 0s 2ms/step - loss: 3.7138e-04\n", 938 | "Epoch 414/500\n", 939 | "1/1 [==============================] - 0s 3ms/step - loss: 3.6375e-04\n", 940 | "Epoch 415/500\n", 941 | "1/1 [==============================] - 0s 1ms/step - loss: 3.5628e-04\n", 942 | "Epoch 416/500\n", 943 | "1/1 [==============================] - 0s 2ms/step - loss: 3.4896e-04\n", 944 | "Epoch 417/500\n", 945 | "1/1 [==============================] - 0s 2ms/step - loss: 3.4180e-04\n", 946 | "Epoch 418/500\n", 947 | "1/1 [==============================] - 0s 2ms/step - loss: 3.3477e-04\n", 948 | "Epoch 419/500\n", 949 | "1/1 [==============================] - 0s 3ms/step - loss: 3.2790e-04\n", 950 | "Epoch 420/500\n", 951 | "1/1 [==============================] - 0s 2ms/step - loss: 3.2116e-04\n", 952 | "Epoch 421/500\n", 953 | "1/1 [==============================] - 0s 1ms/step - loss: 3.1456e-04\n", 954 | "Epoch 422/500\n", 955 | "1/1 [==============================] - 0s 2ms/step - loss: 3.0810e-04\n", 956 | "Epoch 423/500\n", 957 | "1/1 [==============================] - 0s 2ms/step - loss: 3.0177e-04\n", 958 | "Epoch 424/500\n", 959 | "1/1 [==============================] - 0s 1ms/step - loss: 2.9558e-04\n", 960 | "Epoch 425/500\n", 961 | "1/1 [==============================] - 0s 1ms/step - loss: 2.8951e-04\n", 962 | "Epoch 426/500\n", 963 | "1/1 [==============================] - 0s 2ms/step - loss: 2.8356e-04\n", 964 | "Epoch 427/500\n", 965 | "1/1 [==============================] - 0s 2ms/step - loss: 2.7773e-04\n", 966 | "Epoch 428/500\n", 967 | "1/1 [==============================] - 0s 2ms/step - loss: 2.7203e-04\n", 968 | "Epoch 429/500\n", 969 | "1/1 [==============================] - 0s 1ms/step - loss: 2.6644e-04\n", 970 | "Epoch 430/500\n", 971 | "1/1 [==============================] - 0s 3ms/step - loss: 2.6097e-04\n", 972 | "Epoch 431/500\n", 973 | "1/1 [==============================] - 0s 2ms/step - loss: 2.5561e-04\n", 974 | "Epoch 432/500\n", 975 | "1/1 [==============================] - 0s 2ms/step - loss: 2.5036e-04\n", 976 | "Epoch 433/500\n", 977 | "1/1 [==============================] - 0s 2ms/step - loss: 2.4522e-04\n", 978 | "Epoch 434/500\n", 979 | "1/1 [==============================] - 0s 2ms/step - loss: 2.4018e-04\n", 980 | "Epoch 435/500\n", 981 | "1/1 [==============================] - 0s 2ms/step - loss: 2.3525e-04\n", 982 | "Epoch 436/500\n", 983 | "1/1 [==============================] - 0s 2ms/step - loss: 2.3041e-04\n", 984 | "Epoch 437/500\n", 985 | "1/1 [==============================] - 0s 3ms/step - loss: 2.2568e-04\n", 986 | "Epoch 438/500\n", 987 | "1/1 [==============================] - 0s 3ms/step - loss: 2.2104e-04\n", 988 | "Epoch 439/500\n", 989 | "1/1 [==============================] - 0s 4ms/step - loss: 2.1651e-04\n", 990 | "Epoch 440/500\n", 991 | "1/1 [==============================] - 0s 2ms/step - loss: 2.1206e-04\n", 992 | "Epoch 441/500\n", 993 | "1/1 [==============================] - 0s 2ms/step - loss: 2.0770e-04\n", 994 | "Epoch 442/500\n", 995 | "1/1 [==============================] - 0s 2ms/step - loss: 2.0344e-04\n", 996 | "Epoch 443/500\n", 997 | "1/1 [==============================] - 0s 2ms/step - loss: 1.9926e-04\n", 998 | "Epoch 444/500\n", 999 | "1/1 [==============================] - 0s 2ms/step - loss: 1.9516e-04\n", 1000 | "Epoch 445/500\n", 1001 | "1/1 [==============================] - 0s 2ms/step - loss: 1.9115e-04\n", 1002 | "Epoch 446/500\n", 1003 | "1/1 [==============================] - 0s 2ms/step - loss: 1.8723e-04\n", 1004 | "Epoch 447/500\n", 1005 | "1/1 [==============================] - 0s 3ms/step - loss: 1.8338e-04\n", 1006 | "Epoch 448/500\n", 1007 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7962e-04\n", 1008 | "Epoch 449/500\n", 1009 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7593e-04\n", 1010 | "Epoch 450/500\n", 1011 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7231e-04\n", 1012 | "Epoch 451/500\n", 1013 | "1/1 [==============================] - 0s 2ms/step - loss: 1.6877e-04\n", 1014 | "Epoch 452/500\n", 1015 | "1/1 [==============================] - 0s 6ms/step - loss: 1.6530e-04\n", 1016 | "Epoch 453/500\n", 1017 | "1/1 [==============================] - 0s 2ms/step - loss: 1.6191e-04\n", 1018 | "Epoch 454/500\n", 1019 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5859e-04\n", 1020 | "Epoch 455/500\n", 1021 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5533e-04\n", 1022 | "Epoch 456/500\n", 1023 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5214e-04\n", 1024 | "Epoch 457/500\n", 1025 | "1/1 [==============================] - 0s 2ms/step - loss: 1.4901e-04\n", 1026 | "Epoch 458/500\n", 1027 | "1/1 [==============================] - 0s 3ms/step - loss: 1.4595e-04\n", 1028 | "Epoch 459/500\n", 1029 | "1/1 [==============================] - 0s 2ms/step - loss: 1.4295e-04\n", 1030 | "Epoch 460/500\n", 1031 | "1/1 [==============================] - 0s 2ms/step - loss: 1.4002e-04\n", 1032 | "Epoch 461/500\n", 1033 | "1/1 [==============================] - 0s 2ms/step - loss: 1.3714e-04\n", 1034 | "Epoch 462/500\n", 1035 | "1/1 [==============================] - 0s 2ms/step - loss: 1.3432e-04\n", 1036 | "Epoch 463/500\n", 1037 | "1/1 [==============================] - 0s 2ms/step - loss: 1.3156e-04\n", 1038 | "Epoch 464/500\n", 1039 | "1/1 [==============================] - 0s 3ms/step - loss: 1.2886e-04\n", 1040 | "Epoch 465/500\n", 1041 | "1/1 [==============================] - 0s 2ms/step - loss: 1.2622e-04\n", 1042 | "Epoch 466/500\n", 1043 | "1/1 [==============================] - 0s 2ms/step - loss: 1.2362e-04\n", 1044 | "Epoch 467/500\n", 1045 | "1/1 [==============================] - 0s 2ms/step - loss: 1.2108e-04\n", 1046 | "Epoch 468/500\n", 1047 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1860e-04\n", 1048 | "Epoch 469/500\n", 1049 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1616e-04\n", 1050 | "Epoch 470/500\n", 1051 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1377e-04\n", 1052 | "Epoch 471/500\n", 1053 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1144e-04\n", 1054 | "Epoch 472/500\n", 1055 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0915e-04\n", 1056 | "Epoch 473/500\n", 1057 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0690e-04\n", 1058 | "Epoch 474/500\n", 1059 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0471e-04\n", 1060 | "Epoch 475/500\n", 1061 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0256e-04\n", 1062 | "Epoch 476/500\n", 1063 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0045e-04\n", 1064 | "Epoch 477/500\n", 1065 | "1/1 [==============================] - 0s 2ms/step - loss: 9.8387e-05\n", 1066 | "Epoch 478/500\n", 1067 | "1/1 [==============================] - 0s 2ms/step - loss: 9.6366e-05\n", 1068 | "Epoch 479/500\n", 1069 | "1/1 [==============================] - 0s 2ms/step - loss: 9.4388e-05\n", 1070 | "Epoch 480/500\n", 1071 | "1/1 [==============================] - 0s 1ms/step - loss: 9.2449e-05\n", 1072 | "Epoch 481/500\n", 1073 | "1/1 [==============================] - 0s 2ms/step - loss: 9.0549e-05\n", 1074 | "Epoch 482/500\n", 1075 | "1/1 [==============================] - 0s 2ms/step - loss: 8.8690e-05\n", 1076 | "Epoch 483/500\n", 1077 | "1/1 [==============================] - 0s 2ms/step - loss: 8.6868e-05\n", 1078 | "Epoch 484/500\n", 1079 | "1/1 [==============================] - 0s 2ms/step - loss: 8.5084e-05\n", 1080 | "Epoch 485/500\n", 1081 | "1/1 [==============================] - 0s 3ms/step - loss: 8.3338e-05\n", 1082 | "Epoch 486/500\n", 1083 | "1/1 [==============================] - 0s 2ms/step - loss: 8.1625e-05\n", 1084 | "Epoch 487/500\n", 1085 | "1/1 [==============================] - 0s 2ms/step - loss: 7.9948e-05\n", 1086 | "Epoch 488/500\n", 1087 | "1/1 [==============================] - 0s 2ms/step - loss: 7.8307e-05\n", 1088 | "Epoch 489/500\n", 1089 | "1/1 [==============================] - 0s 2ms/step - loss: 7.6699e-05\n", 1090 | "Epoch 490/500\n", 1091 | "1/1 [==============================] - 0s 2ms/step - loss: 7.5124e-05\n", 1092 | "Epoch 491/500\n", 1093 | "1/1 [==============================] - 0s 2ms/step - loss: 7.3580e-05\n", 1094 | "Epoch 492/500\n", 1095 | "1/1 [==============================] - 0s 2ms/step - loss: 7.2068e-05\n", 1096 | "Epoch 493/500\n", 1097 | "1/1 [==============================] - 0s 3ms/step - loss: 7.0588e-05\n", 1098 | "Epoch 494/500\n", 1099 | "1/1 [==============================] - 0s 2ms/step - loss: 6.9139e-05\n", 1100 | "Epoch 495/500\n", 1101 | "1/1 [==============================] - 0s 1ms/step - loss: 6.7718e-05\n", 1102 | "Epoch 496/500\n", 1103 | "1/1 [==============================] - 0s 2ms/step - loss: 6.6327e-05\n", 1104 | "Epoch 497/500\n", 1105 | "1/1 [==============================] - 0s 2ms/step - loss: 6.4964e-05\n", 1106 | "Epoch 498/500\n", 1107 | "1/1 [==============================] - 0s 2ms/step - loss: 6.3631e-05\n", 1108 | "Epoch 499/500\n", 1109 | "1/1 [==============================] - 0s 2ms/step - loss: 6.2324e-05\n", 1110 | "Epoch 500/500\n", 1111 | "1/1 [==============================] - 0s 2ms/step - loss: 6.1043e-05\n" 1112 | ], 1113 | "name": "stdout" 1114 | }, 1115 | { 1116 | "output_type": "execute_result", 1117 | "data": { 1118 | "text/plain": [ 1119 | "" 1120 | ] 1121 | }, 1122 | "metadata": { 1123 | "tags": [] 1124 | }, 1125 | "execution_count": 21 1126 | } 1127 | ] 1128 | }, 1129 | { 1130 | "cell_type": "code", 1131 | "metadata": { 1132 | "id": "MrIWycDqZqUx", 1133 | "colab_type": "code", 1134 | "colab": { 1135 | "base_uri": "https://localhost:8080/", 1136 | "height": 72 1137 | }, 1138 | "outputId": "ad7a7e29-2bf2-440a-d00e-83ce14c1e985" 1139 | }, 1140 | "source": [ 1141 | "print(model.predict([2]))" 1142 | ], 1143 | "execution_count": 23, 1144 | "outputs": [ 1145 | { 1146 | "output_type": "stream", 1147 | "text": [ 1148 | "WARNING:tensorflow:5 out of the last 7 calls to .predict_function at 0x7f514dd86950> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", 1149 | "[[3.003635]]\n" 1150 | ], 1151 | "name": "stdout" 1152 | } 1153 | ] 1154 | } 1155 | ] 1156 | } -------------------------------------------------------------------------------- /Linear_Regression(NumPY,_Pandas_etc).ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Linear Regression(NumPY, Pandas etc).ipynb", 7 | "provenance": [], 8 | "collapsed_sections": [], 9 | "authorship_tag": "ABX9TyOodJ6grrhpespFSzKKs1E7", 10 | "include_colab_link": true 11 | }, 12 | "kernelspec": { 13 | "name": "python3", 14 | "display_name": "Python 3" 15 | }, 16 | "language_info": { 17 | "name": "python" 18 | } 19 | }, 20 | "cells": [ 21 | { 22 | "cell_type": "markdown", 23 | "metadata": { 24 | "id": "view-in-github", 25 | "colab_type": "text" 26 | }, 27 | "source": [ 28 | "\"Open" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "metadata": { 34 | "id": "y-zGxFGZYyl0" 35 | }, 36 | "source": [ 37 | "import pandas as pd\n", 38 | "import numpy as np" 39 | ], 40 | "execution_count": 27, 41 | "outputs": [] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "metadata": { 46 | "colab": { 47 | "base_uri": "https://localhost:8080/" 48 | }, 49 | "id": "MSV8JbcZY1Ny", 50 | "outputId": "6dc57c95-0d6e-4af7-f620-3a8b8abeb6fc" 51 | }, 52 | "source": [ 53 | "df = pd.read_csv('/content/Levels_Fyi_Salary_Data.csv')\n", 54 | "print(df)" 55 | ], 56 | "execution_count": 9, 57 | "outputs": [ 58 | { 59 | "output_type": "stream", 60 | "name": "stdout", 61 | "text": [ 62 | " timestamp company level ... Race_Hispanic Race Education\n", 63 | "0 6/7/2017 11:33:27 Oracle L3 ... 0 NaN NaN\n", 64 | "1 6/10/2017 17:11:29 eBay SE 2 ... 0 NaN NaN\n", 65 | "2 6/11/2017 14:53:57 Amazon L7 ... 0 NaN NaN\n", 66 | "3 6/17/2017 0:23:14 Apple M1 ... 0 NaN NaN\n", 67 | "4 6/20/2017 10:58:51 Microsoft 60 ... 0 NaN NaN\n", 68 | "... ... ... ... ... ... ... ...\n", 69 | "62637 9/9/2018 11:52:32 Google T4 ... 0 NaN NaN\n", 70 | "62638 9/13/2018 8:23:32 Microsoft 62 ... 0 NaN NaN\n", 71 | "62639 9/13/2018 14:35:59 MSFT 63 ... 0 NaN NaN\n", 72 | "62640 9/16/2018 16:10:35 Salesforce Lead MTS ... 0 NaN NaN\n", 73 | "62641 1/29/2019 5:12:59 apple ict3 ... 0 NaN NaN\n", 74 | "\n", 75 | "[62642 rows x 29 columns]\n" 76 | ] 77 | } 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "metadata": { 83 | "colab": { 84 | "base_uri": "https://localhost:8080/" 85 | }, 86 | "id": "eTPzeV3aZs04", 87 | "outputId": "95a28ad6-2a8b-44db-dd78-de4351655256" 88 | }, 89 | "source": [ 90 | "print(df.columns)" 91 | ], 92 | "execution_count": 10, 93 | "outputs": [ 94 | { 95 | "output_type": "stream", 96 | "name": "stdout", 97 | "text": [ 98 | "Index(['timestamp', 'company', 'level', 'title', 'totalyearlycompensation',\n", 99 | " 'location', 'yearsofexperience', 'yearsatcompany', 'tag', 'basesalary',\n", 100 | " 'stockgrantvalue', 'bonus', 'gender', 'otherdetails', 'cityid', 'dmaid',\n", 101 | " 'rowNumber', 'Masters_Degree', 'Bachelors_Degree', 'Doctorate_Degree',\n", 102 | " 'Highschool', 'Some_College', 'Race_Asian', 'Race_White',\n", 103 | " 'Race_Two_Or_More', 'Race_Black', 'Race_Hispanic', 'Race', 'Education'],\n", 104 | " dtype='object')\n" 105 | ] 106 | } 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "metadata": { 112 | "id": "Iac8lT_iZ4Pu" 113 | }, 114 | "source": [ 115 | "salaryDF = df[['company', 'totalyearlycompensation', 'yearsofexperience', 'yearsatcompany', 'basesalary']]" 116 | ], 117 | "execution_count": 11, 118 | "outputs": [] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "metadata": { 123 | "colab": { 124 | "base_uri": "https://localhost:8080/" 125 | }, 126 | "id": "zEN0R2zYaaKy", 127 | "outputId": "fe4785f9-938f-432e-e213-cb46d35cd871" 128 | }, 129 | "source": [ 130 | "print(salaryDF.head())" 131 | ], 132 | "execution_count": 12, 133 | "outputs": [ 134 | { 135 | "output_type": "stream", 136 | "name": "stdout", 137 | "text": [ 138 | " company totalyearlycompensation ... yearsatcompany basesalary\n", 139 | "0 Oracle 127000 ... 1.5 107000.0\n", 140 | "1 eBay 100000 ... 3.0 0.0\n", 141 | "2 Amazon 310000 ... 0.0 155000.0\n", 142 | "3 Apple 372000 ... 5.0 157000.0\n", 143 | "4 Microsoft 157000 ... 3.0 0.0\n", 144 | "\n", 145 | "[5 rows x 5 columns]\n" 146 | ] 147 | } 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "metadata": { 153 | "id": "ka6aCbUlBCUM" 154 | }, 155 | "source": [ 156 | "## Create training and testing data" 157 | ] 158 | }, 159 | { 160 | "cell_type": "markdown", 161 | "metadata": { 162 | "id": "u3e3eb06BN4s" 163 | }, 164 | "source": [ 165 | "## Functions for RMSE & calculating weights" 166 | ] 167 | }, 168 | { 169 | "cell_type": "code", 170 | "metadata": { 171 | "id": "V5grJVNi4omx" 172 | }, 173 | "source": [ 174 | "def rmse(targets, predictions):\n", 175 | " return np.sqrt((np.square(predictions - targets)).mean())\n", 176 | "\n", 177 | "def calculateWeights(x_train, y_train):\n", 178 | " w = np.linalg.inv(np.transpose(x_train).dot(x_train)).dot(np.transpose(x_train).dot(y_train))\n", 179 | " return w\n" 180 | ], 181 | "execution_count": 53, 182 | "outputs": [] 183 | } 184 | ] 185 | } -------------------------------------------------------------------------------- /Machine Learning Roadmap for Absolute Beginners.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/smithakolan/MachineLearningFundamentals/876608944e2666dd7b8900a6f57092d73124f5fd/Machine Learning Roadmap for Absolute Beginners.pdf -------------------------------------------------------------------------------- /MelbourneHousing.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "MelbourneHousing.ipynb", 7 | "provenance": [], 8 | "authorship_tag": "ABX9TyOiZAIvlBAGHMAM8s0PLCg/", 9 | "include_colab_link": true 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | } 15 | }, 16 | "cells": [ 17 | { 18 | "cell_type": "markdown", 19 | "metadata": { 20 | "id": "view-in-github", 21 | "colab_type": "text" 22 | }, 23 | "source": [ 24 | "\"Open" 25 | ] 26 | }, 27 | { 28 | "cell_type": "markdown", 29 | "metadata": { 30 | "id": "MNg-eea0N144", 31 | "colab_type": "text" 32 | }, 33 | "source": [ 34 | "In this exercise you'll try to build a neural network that predicts the price of a house according to a simple formula.\n", 35 | "\n", 36 | "So, imagine if house pricing was as easy as a house costs 50k + 50k per bedroom, so that a 1 bedroom house costs 100k, a 2 bedroom house costs 150k etc.\n", 37 | "\n", 38 | "How would you create a neural network that learns this relationship so that it would predict a 7 bedroom house as costing close to 400k etc.\n", 39 | "\n", 40 | "Hint: Your network might work better if you scale the house price down. You don't have to give the answer 400...it might be better to create something that predicts the number 4, and then your answer is in the 'hundreds of thousands' etc." 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "metadata": { 46 | "id": "IJNZltOZN3J9", 47 | "colab_type": "code", 48 | "colab": {} 49 | }, 50 | "source": [ 51 | "import tensorflow as tf\n", 52 | "import numpy as np\n", 53 | "from tensorflow import keras" 54 | ], 55 | "execution_count": 1, 56 | "outputs": [] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "metadata": { 61 | "id": "ua35REl6N6Qg", 62 | "colab_type": "code", 63 | "colab": {} 64 | }, 65 | "source": [ 66 | "#house_model\n", 67 | "def house_model(y_new):\n", 68 | " xs = [0,1,2,3,4,5,6]\n", 69 | " ys = [0.5,1,1.5,2,2.5,3,3.5]\n", 70 | " model = keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])\n", 71 | " model.compile(optimizer=\"sgd\", loss='mean_squared_error')\n", 72 | " model.fit(xs, ys, epochs=500)\n", 73 | " return round(model.predict(y_new)[0][0])" 74 | ], 75 | "execution_count": 2, 76 | "outputs": [] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "metadata": { 81 | "id": "8v8lVtthN_Jb", 82 | "colab_type": "code", 83 | "colab": { 84 | "base_uri": "https://localhost:8080/", 85 | "height": 1000 86 | }, 87 | "outputId": "33de8946-2fe8-4f67-f4fa-bdbe0fc387a5" 88 | }, 89 | "source": [ 90 | "prediction = house_model([7.0])\n", 91 | "print(prediction, \"hundred thousand dollars\")" 92 | ], 93 | "execution_count": 3, 94 | "outputs": [ 95 | { 96 | "output_type": "stream", 97 | "text": [ 98 | "Epoch 1/500\n", 99 | "1/1 [==============================] - 0s 4ms/step - loss: 55.0992\n", 100 | "Epoch 2/500\n", 101 | "1/1 [==============================] - 0s 2ms/step - loss: 29.0286\n", 102 | "Epoch 3/500\n", 103 | "1/1 [==============================] - 0s 1ms/step - loss: 15.2936\n", 104 | "Epoch 4/500\n", 105 | "1/1 [==============================] - 0s 2ms/step - loss: 8.0574\n", 106 | "Epoch 5/500\n", 107 | "1/1 [==============================] - 0s 1ms/step - loss: 4.2452\n", 108 | "Epoch 6/500\n", 109 | "1/1 [==============================] - 0s 2ms/step - loss: 2.2367\n", 110 | "Epoch 7/500\n", 111 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1786\n", 112 | "Epoch 8/500\n", 113 | "1/1 [==============================] - 0s 3ms/step - loss: 0.6211\n", 114 | "Epoch 9/500\n", 115 | "1/1 [==============================] - 0s 2ms/step - loss: 0.3274\n", 116 | "Epoch 10/500\n", 117 | "1/1 [==============================] - 0s 1ms/step - loss: 0.1727\n", 118 | "Epoch 11/500\n", 119 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0912\n", 120 | "Epoch 12/500\n", 121 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0482\n", 122 | "Epoch 13/500\n", 123 | "1/1 [==============================] - 0s 2ms/step - loss: 0.0256\n", 124 | "Epoch 14/500\n", 125 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0137\n", 126 | "Epoch 15/500\n", 127 | "1/1 [==============================] - 0s 3ms/step - loss: 0.0074\n", 128 | "Epoch 16/500\n", 129 | "1/1 [==============================] - 0s 3ms/step - loss: 0.0041\n", 130 | "Epoch 17/500\n", 131 | "1/1 [==============================] - 0s 4ms/step - loss: 0.0023\n", 132 | "Epoch 18/500\n", 133 | "1/1 [==============================] - 0s 1ms/step - loss: 0.0014\n", 134 | "Epoch 19/500\n", 135 | "1/1 [==============================] - 0s 1ms/step - loss: 9.2567e-04\n", 136 | "Epoch 20/500\n", 137 | "1/1 [==============================] - 0s 2ms/step - loss: 6.6628e-04\n", 138 | "Epoch 21/500\n", 139 | "1/1 [==============================] - 0s 2ms/step - loss: 5.2755e-04\n", 140 | "Epoch 22/500\n", 141 | "1/1 [==============================] - 0s 2ms/step - loss: 4.5240e-04\n", 142 | "Epoch 23/500\n", 143 | "1/1 [==============================] - 0s 4ms/step - loss: 4.1078e-04\n", 144 | "Epoch 24/500\n", 145 | "1/1 [==============================] - 0s 2ms/step - loss: 3.8685e-04\n", 146 | "Epoch 25/500\n", 147 | "1/1 [==============================] - 0s 2ms/step - loss: 3.7226e-04\n", 148 | "Epoch 26/500\n", 149 | "1/1 [==============================] - 0s 2ms/step - loss: 3.6261e-04\n", 150 | "Epoch 27/500\n", 151 | "1/1 [==============================] - 0s 1ms/step - loss: 3.5559e-04\n", 152 | "Epoch 28/500\n", 153 | "1/1 [==============================] - 0s 3ms/step - loss: 3.4998e-04\n", 154 | "Epoch 29/500\n", 155 | "1/1 [==============================] - 0s 2ms/step - loss: 3.4513e-04\n", 156 | "Epoch 30/500\n", 157 | "1/1 [==============================] - 0s 1ms/step - loss: 3.4070e-04\n", 158 | "Epoch 31/500\n", 159 | "1/1 [==============================] - 0s 1ms/step - loss: 3.3652e-04\n", 160 | "Epoch 32/500\n", 161 | "1/1 [==============================] - 0s 2ms/step - loss: 3.3249e-04\n", 162 | "Epoch 33/500\n", 163 | "1/1 [==============================] - 0s 2ms/step - loss: 3.2856e-04\n", 164 | "Epoch 34/500\n", 165 | "1/1 [==============================] - 0s 4ms/step - loss: 3.2470e-04\n", 166 | "Epoch 35/500\n", 167 | "1/1 [==============================] - 0s 2ms/step - loss: 3.2091e-04\n", 168 | "Epoch 36/500\n", 169 | "1/1 [==============================] - 0s 2ms/step - loss: 3.1716e-04\n", 170 | "Epoch 37/500\n", 171 | "1/1 [==============================] - 0s 6ms/step - loss: 3.1347e-04\n", 172 | "Epoch 38/500\n", 173 | "1/1 [==============================] - 0s 2ms/step - loss: 3.0982e-04\n", 174 | "Epoch 39/500\n", 175 | "1/1 [==============================] - 0s 2ms/step - loss: 3.0621e-04\n", 176 | "Epoch 40/500\n", 177 | "1/1 [==============================] - 0s 1ms/step - loss: 3.0265e-04\n", 178 | "Epoch 41/500\n", 179 | "1/1 [==============================] - 0s 3ms/step - loss: 2.9912e-04\n", 180 | "Epoch 42/500\n", 181 | "1/1 [==============================] - 0s 1ms/step - loss: 2.9564e-04\n", 182 | "Epoch 43/500\n", 183 | "1/1 [==============================] - 0s 1ms/step - loss: 2.9220e-04\n", 184 | "Epoch 44/500\n", 185 | "1/1 [==============================] - 0s 3ms/step - loss: 2.8880e-04\n", 186 | "Epoch 45/500\n", 187 | "1/1 [==============================] - 0s 2ms/step - loss: 2.8544e-04\n", 188 | "Epoch 46/500\n", 189 | "1/1 [==============================] - 0s 1ms/step - loss: 2.8212e-04\n", 190 | "Epoch 47/500\n", 191 | "1/1 [==============================] - 0s 2ms/step - loss: 2.7884e-04\n", 192 | "Epoch 48/500\n", 193 | "1/1 [==============================] - 0s 2ms/step - loss: 2.7559e-04\n", 194 | "Epoch 49/500\n", 195 | "1/1 [==============================] - 0s 2ms/step - loss: 2.7238e-04\n", 196 | "Epoch 50/500\n", 197 | "1/1 [==============================] - 0s 4ms/step - loss: 2.6921e-04\n", 198 | "Epoch 51/500\n", 199 | "1/1 [==============================] - 0s 1ms/step - loss: 2.6608e-04\n", 200 | "Epoch 52/500\n", 201 | "1/1 [==============================] - 0s 1ms/step - loss: 2.6298e-04\n", 202 | "Epoch 53/500\n", 203 | "1/1 [==============================] - 0s 1ms/step - loss: 2.5992e-04\n", 204 | "Epoch 54/500\n", 205 | "1/1 [==============================] - 0s 2ms/step - loss: 2.5690e-04\n", 206 | "Epoch 55/500\n", 207 | "1/1 [==============================] - 0s 2ms/step - loss: 2.5391e-04\n", 208 | "Epoch 56/500\n", 209 | "1/1 [==============================] - 0s 1ms/step - loss: 2.5095e-04\n", 210 | "Epoch 57/500\n", 211 | "1/1 [==============================] - 0s 1ms/step - loss: 2.4803e-04\n", 212 | "Epoch 58/500\n", 213 | "1/1 [==============================] - 0s 1ms/step - loss: 2.4515e-04\n", 214 | "Epoch 59/500\n", 215 | "1/1 [==============================] - 0s 1ms/step - loss: 2.4229e-04\n", 216 | "Epoch 60/500\n", 217 | "1/1 [==============================] - 0s 2ms/step - loss: 2.3947e-04\n", 218 | "Epoch 61/500\n", 219 | "1/1 [==============================] - 0s 2ms/step - loss: 2.3669e-04\n", 220 | "Epoch 62/500\n", 221 | "1/1 [==============================] - 0s 1ms/step - loss: 2.3393e-04\n", 222 | "Epoch 63/500\n", 223 | "1/1 [==============================] - 0s 2ms/step - loss: 2.3121e-04\n", 224 | "Epoch 64/500\n", 225 | "1/1 [==============================] - 0s 2ms/step - loss: 2.2852e-04\n", 226 | "Epoch 65/500\n", 227 | "1/1 [==============================] - 0s 2ms/step - loss: 2.2586e-04\n", 228 | "Epoch 66/500\n", 229 | "1/1 [==============================] - 0s 3ms/step - loss: 2.2323e-04\n", 230 | "Epoch 67/500\n", 231 | "1/1 [==============================] - 0s 2ms/step - loss: 2.2063e-04\n", 232 | "Epoch 68/500\n", 233 | "1/1 [==============================] - 0s 1ms/step - loss: 2.1807e-04\n", 234 | "Epoch 69/500\n", 235 | "1/1 [==============================] - 0s 4ms/step - loss: 2.1553e-04\n", 236 | "Epoch 70/500\n", 237 | "1/1 [==============================] - 0s 1ms/step - loss: 2.1302e-04\n", 238 | "Epoch 71/500\n", 239 | "1/1 [==============================] - 0s 2ms/step - loss: 2.1054e-04\n", 240 | "Epoch 72/500\n", 241 | "1/1 [==============================] - 0s 1ms/step - loss: 2.0809e-04\n", 242 | "Epoch 73/500\n", 243 | "1/1 [==============================] - 0s 2ms/step - loss: 2.0567e-04\n", 244 | "Epoch 74/500\n", 245 | "1/1 [==============================] - 0s 3ms/step - loss: 2.0328e-04\n", 246 | "Epoch 75/500\n", 247 | "1/1 [==============================] - 0s 2ms/step - loss: 2.0091e-04\n", 248 | "Epoch 76/500\n", 249 | "1/1 [==============================] - 0s 1ms/step - loss: 1.9857e-04\n", 250 | "Epoch 77/500\n", 251 | "1/1 [==============================] - 0s 2ms/step - loss: 1.9626e-04\n", 252 | "Epoch 78/500\n", 253 | "1/1 [==============================] - 0s 2ms/step - loss: 1.9398e-04\n", 254 | "Epoch 79/500\n", 255 | "1/1 [==============================] - 0s 1ms/step - loss: 1.9172e-04\n", 256 | "Epoch 80/500\n", 257 | "1/1 [==============================] - 0s 2ms/step - loss: 1.8949e-04\n", 258 | "Epoch 81/500\n", 259 | "1/1 [==============================] - 0s 2ms/step - loss: 1.8728e-04\n", 260 | "Epoch 82/500\n", 261 | "1/1 [==============================] - 0s 2ms/step - loss: 1.8510e-04\n", 262 | "Epoch 83/500\n", 263 | "1/1 [==============================] - 0s 1ms/step - loss: 1.8295e-04\n", 264 | "Epoch 84/500\n", 265 | "1/1 [==============================] - 0s 2ms/step - loss: 1.8082e-04\n", 266 | "Epoch 85/500\n", 267 | "1/1 [==============================] - 0s 1ms/step - loss: 1.7872e-04\n", 268 | "Epoch 86/500\n", 269 | "1/1 [==============================] - 0s 1ms/step - loss: 1.7664e-04\n", 270 | "Epoch 87/500\n", 271 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7458e-04\n", 272 | "Epoch 88/500\n", 273 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7255e-04\n", 274 | "Epoch 89/500\n", 275 | "1/1 [==============================] - 0s 3ms/step - loss: 1.7054e-04\n", 276 | "Epoch 90/500\n", 277 | "1/1 [==============================] - 0s 4ms/step - loss: 1.6856e-04\n", 278 | "Epoch 91/500\n", 279 | "1/1 [==============================] - 0s 1ms/step - loss: 1.6659e-04\n", 280 | "Epoch 92/500\n", 281 | "1/1 [==============================] - 0s 2ms/step - loss: 1.6466e-04\n", 282 | "Epoch 93/500\n", 283 | "1/1 [==============================] - 0s 2ms/step - loss: 1.6274e-04\n", 284 | "Epoch 94/500\n", 285 | "1/1 [==============================] - 0s 2ms/step - loss: 1.6085e-04\n", 286 | "Epoch 95/500\n", 287 | "1/1 [==============================] - 0s 6ms/step - loss: 1.5897e-04\n", 288 | "Epoch 96/500\n", 289 | "1/1 [==============================] - 0s 3ms/step - loss: 1.5712e-04\n", 290 | "Epoch 97/500\n", 291 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5530e-04\n", 292 | "Epoch 98/500\n", 293 | "1/1 [==============================] - 0s 1ms/step - loss: 1.5349e-04\n", 294 | "Epoch 99/500\n", 295 | "1/1 [==============================] - 0s 1ms/step - loss: 1.5170e-04\n", 296 | "Epoch 100/500\n", 297 | "1/1 [==============================] - 0s 2ms/step - loss: 1.4994e-04\n", 298 | "Epoch 101/500\n", 299 | "1/1 [==============================] - 0s 1ms/step - loss: 1.4819e-04\n", 300 | "Epoch 102/500\n", 301 | "1/1 [==============================] - 0s 2ms/step - loss: 1.4647e-04\n", 302 | "Epoch 103/500\n", 303 | "1/1 [==============================] - 0s 1ms/step - loss: 1.4476e-04\n", 304 | "Epoch 104/500\n", 305 | "1/1 [==============================] - 0s 1ms/step - loss: 1.4308e-04\n", 306 | "Epoch 105/500\n", 307 | "1/1 [==============================] - 0s 1ms/step - loss: 1.4141e-04\n", 308 | "Epoch 106/500\n", 309 | "1/1 [==============================] - 0s 1ms/step - loss: 1.3977e-04\n", 310 | "Epoch 107/500\n", 311 | "1/1 [==============================] - 0s 1ms/step - loss: 1.3814e-04\n", 312 | "Epoch 108/500\n", 313 | "1/1 [==============================] - 0s 1ms/step - loss: 1.3653e-04\n", 314 | "Epoch 109/500\n", 315 | "1/1 [==============================] - 0s 1ms/step - loss: 1.3494e-04\n", 316 | "Epoch 110/500\n", 317 | "1/1 [==============================] - 0s 1ms/step - loss: 1.3337e-04\n", 318 | "Epoch 111/500\n", 319 | "1/1 [==============================] - 0s 2ms/step - loss: 1.3182e-04\n", 320 | "Epoch 112/500\n", 321 | "1/1 [==============================] - 0s 1ms/step - loss: 1.3029e-04\n", 322 | "Epoch 113/500\n", 323 | "1/1 [==============================] - 0s 1ms/step - loss: 1.2877e-04\n", 324 | "Epoch 114/500\n", 325 | "1/1 [==============================] - 0s 1ms/step - loss: 1.2727e-04\n", 326 | "Epoch 115/500\n", 327 | "1/1 [==============================] - 0s 1ms/step - loss: 1.2579e-04\n", 328 | "Epoch 116/500\n", 329 | "1/1 [==============================] - 0s 1ms/step - loss: 1.2433e-04\n", 330 | "Epoch 117/500\n", 331 | "1/1 [==============================] - 0s 2ms/step - loss: 1.2288e-04\n", 332 | "Epoch 118/500\n", 333 | "1/1 [==============================] - 0s 3ms/step - loss: 1.2145e-04\n", 334 | "Epoch 119/500\n", 335 | "1/1 [==============================] - 0s 1ms/step - loss: 1.2004e-04\n", 336 | "Epoch 120/500\n", 337 | "1/1 [==============================] - 0s 1ms/step - loss: 1.1864e-04\n", 338 | "Epoch 121/500\n", 339 | "1/1 [==============================] - 0s 3ms/step - loss: 1.1726e-04\n", 340 | "Epoch 122/500\n", 341 | "1/1 [==============================] - 0s 1ms/step - loss: 1.1589e-04\n", 342 | "Epoch 123/500\n", 343 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1455e-04\n", 344 | "Epoch 124/500\n", 345 | "1/1 [==============================] - 0s 1ms/step - loss: 1.1321e-04\n", 346 | "Epoch 125/500\n", 347 | "1/1 [==============================] - 0s 1ms/step - loss: 1.1189e-04\n", 348 | "Epoch 126/500\n", 349 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1059e-04\n", 350 | "Epoch 127/500\n", 351 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0931e-04\n", 352 | "Epoch 128/500\n", 353 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0803e-04\n", 354 | "Epoch 129/500\n", 355 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0678e-04\n", 356 | "Epoch 130/500\n", 357 | "1/1 [==============================] - 0s 1ms/step - loss: 1.0553e-04\n", 358 | "Epoch 131/500\n", 359 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0431e-04\n", 360 | "Epoch 132/500\n", 361 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0309e-04\n", 362 | "Epoch 133/500\n", 363 | "1/1 [==============================] - 0s 1ms/step - loss: 1.0189e-04\n", 364 | "Epoch 134/500\n", 365 | "1/1 [==============================] - 0s 3ms/step - loss: 1.0071e-04\n", 366 | "Epoch 135/500\n", 367 | "1/1 [==============================] - 0s 2ms/step - loss: 9.9534e-05\n", 368 | "Epoch 136/500\n", 369 | "1/1 [==============================] - 0s 2ms/step - loss: 9.8376e-05\n", 370 | "Epoch 137/500\n", 371 | "1/1 [==============================] - 0s 3ms/step - loss: 9.7231e-05\n", 372 | "Epoch 138/500\n", 373 | "1/1 [==============================] - 0s 1ms/step - loss: 9.6099e-05\n", 374 | "Epoch 139/500\n", 375 | "1/1 [==============================] - 0s 1ms/step - loss: 9.4981e-05\n", 376 | "Epoch 140/500\n", 377 | "1/1 [==============================] - 0s 1ms/step - loss: 9.3875e-05\n", 378 | "Epoch 141/500\n", 379 | "1/1 [==============================] - 0s 1ms/step - loss: 9.2783e-05\n", 380 | "Epoch 142/500\n", 381 | "1/1 [==============================] - 0s 1ms/step - loss: 9.1704e-05\n", 382 | "Epoch 143/500\n", 383 | "1/1 [==============================] - 0s 1ms/step - loss: 9.0636e-05\n", 384 | "Epoch 144/500\n", 385 | "1/1 [==============================] - 0s 2ms/step - loss: 8.9581e-05\n", 386 | "Epoch 145/500\n", 387 | "1/1 [==============================] - 0s 2ms/step - loss: 8.8539e-05\n", 388 | "Epoch 146/500\n", 389 | "1/1 [==============================] - 0s 1ms/step - loss: 8.7509e-05\n", 390 | "Epoch 147/500\n", 391 | "1/1 [==============================] - 0s 2ms/step - loss: 8.6490e-05\n", 392 | "Epoch 148/500\n", 393 | "1/1 [==============================] - 0s 1ms/step - loss: 8.5484e-05\n", 394 | "Epoch 149/500\n", 395 | "1/1 [==============================] - 0s 2ms/step - loss: 8.4489e-05\n", 396 | "Epoch 150/500\n", 397 | "1/1 [==============================] - 0s 6ms/step - loss: 8.3506e-05\n", 398 | "Epoch 151/500\n", 399 | "1/1 [==============================] - 0s 4ms/step - loss: 8.2534e-05\n", 400 | "Epoch 152/500\n", 401 | "1/1 [==============================] - 0s 2ms/step - loss: 8.1574e-05\n", 402 | "Epoch 153/500\n", 403 | "1/1 [==============================] - 0s 2ms/step - loss: 8.0624e-05\n", 404 | "Epoch 154/500\n", 405 | "1/1 [==============================] - 0s 1ms/step - loss: 7.9686e-05\n", 406 | "Epoch 155/500\n", 407 | "1/1 [==============================] - 0s 3ms/step - loss: 7.8758e-05\n", 408 | "Epoch 156/500\n", 409 | "1/1 [==============================] - 0s 2ms/step - loss: 7.7842e-05\n", 410 | "Epoch 157/500\n", 411 | "1/1 [==============================] - 0s 1ms/step - loss: 7.6936e-05\n", 412 | "Epoch 158/500\n", 413 | "1/1 [==============================] - 0s 2ms/step - loss: 7.6041e-05\n", 414 | "Epoch 159/500\n", 415 | "1/1 [==============================] - 0s 3ms/step - loss: 7.5155e-05\n", 416 | "Epoch 160/500\n", 417 | "1/1 [==============================] - 0s 1ms/step - loss: 7.4281e-05\n", 418 | "Epoch 161/500\n", 419 | "1/1 [==============================] - 0s 4ms/step - loss: 7.3416e-05\n", 420 | "Epoch 162/500\n", 421 | "1/1 [==============================] - 0s 3ms/step - loss: 7.2562e-05\n", 422 | "Epoch 163/500\n", 423 | "1/1 [==============================] - 0s 2ms/step - loss: 7.1717e-05\n", 424 | "Epoch 164/500\n", 425 | "1/1 [==============================] - 0s 1ms/step - loss: 7.0883e-05\n", 426 | "Epoch 165/500\n", 427 | "1/1 [==============================] - 0s 1ms/step - loss: 7.0058e-05\n", 428 | "Epoch 166/500\n", 429 | "1/1 [==============================] - 0s 2ms/step - loss: 6.9242e-05\n", 430 | "Epoch 167/500\n", 431 | "1/1 [==============================] - 0s 1ms/step - loss: 6.8436e-05\n", 432 | "Epoch 168/500\n", 433 | "1/1 [==============================] - 0s 1ms/step - loss: 6.7640e-05\n", 434 | "Epoch 169/500\n", 435 | "1/1 [==============================] - 0s 1ms/step - loss: 6.6853e-05\n", 436 | "Epoch 170/500\n", 437 | "1/1 [==============================] - 0s 1ms/step - loss: 6.6075e-05\n", 438 | "Epoch 171/500\n", 439 | "1/1 [==============================] - 0s 2ms/step - loss: 6.5306e-05\n", 440 | "Epoch 172/500\n", 441 | "1/1 [==============================] - 0s 1ms/step - loss: 6.4546e-05\n", 442 | "Epoch 173/500\n", 443 | "1/1 [==============================] - 0s 2ms/step - loss: 6.3794e-05\n", 444 | "Epoch 174/500\n", 445 | "1/1 [==============================] - 0s 2ms/step - loss: 6.3052e-05\n", 446 | "Epoch 175/500\n", 447 | "1/1 [==============================] - 0s 2ms/step - loss: 6.2318e-05\n", 448 | "Epoch 176/500\n", 449 | "1/1 [==============================] - 0s 2ms/step - loss: 6.1593e-05\n", 450 | "Epoch 177/500\n", 451 | "1/1 [==============================] - 0s 1ms/step - loss: 6.0876e-05\n", 452 | "Epoch 178/500\n", 453 | "1/1 [==============================] - 0s 5ms/step - loss: 6.0168e-05\n", 454 | "Epoch 179/500\n", 455 | "1/1 [==============================] - 0s 2ms/step - loss: 5.9467e-05\n", 456 | "Epoch 180/500\n", 457 | "1/1 [==============================] - 0s 2ms/step - loss: 5.8775e-05\n", 458 | "Epoch 181/500\n", 459 | "1/1 [==============================] - 0s 1ms/step - loss: 5.8091e-05\n", 460 | "Epoch 182/500\n", 461 | "1/1 [==============================] - 0s 3ms/step - loss: 5.7415e-05\n", 462 | "Epoch 183/500\n", 463 | "1/1 [==============================] - 0s 2ms/step - loss: 5.6746e-05\n", 464 | "Epoch 184/500\n", 465 | "1/1 [==============================] - 0s 1ms/step - loss: 5.6087e-05\n", 466 | "Epoch 185/500\n", 467 | "1/1 [==============================] - 0s 2ms/step - loss: 5.5434e-05\n", 468 | "Epoch 186/500\n", 469 | "1/1 [==============================] - 0s 2ms/step - loss: 5.4788e-05\n", 470 | "Epoch 187/500\n", 471 | "1/1 [==============================] - 0s 2ms/step - loss: 5.4151e-05\n", 472 | "Epoch 188/500\n", 473 | "1/1 [==============================] - 0s 1ms/step - loss: 5.3521e-05\n", 474 | "Epoch 189/500\n", 475 | "1/1 [==============================] - 0s 1ms/step - loss: 5.2898e-05\n", 476 | "Epoch 190/500\n", 477 | "1/1 [==============================] - 0s 2ms/step - loss: 5.2283e-05\n", 478 | "Epoch 191/500\n", 479 | "1/1 [==============================] - 0s 1ms/step - loss: 5.1674e-05\n", 480 | "Epoch 192/500\n", 481 | "1/1 [==============================] - 0s 2ms/step - loss: 5.1073e-05\n", 482 | "Epoch 193/500\n", 483 | "1/1 [==============================] - 0s 1ms/step - loss: 5.0479e-05\n", 484 | "Epoch 194/500\n", 485 | "1/1 [==============================] - 0s 2ms/step - loss: 4.9891e-05\n", 486 | "Epoch 195/500\n", 487 | "1/1 [==============================] - 0s 2ms/step - loss: 4.9310e-05\n", 488 | "Epoch 196/500\n", 489 | "1/1 [==============================] - 0s 1ms/step - loss: 4.8736e-05\n", 490 | "Epoch 197/500\n", 491 | "1/1 [==============================] - 0s 2ms/step - loss: 4.8169e-05\n", 492 | "Epoch 198/500\n", 493 | "1/1 [==============================] - 0s 1ms/step - loss: 4.7608e-05\n", 494 | "Epoch 199/500\n", 495 | "1/1 [==============================] - 0s 1ms/step - loss: 4.7054e-05\n", 496 | "Epoch 200/500\n", 497 | "1/1 [==============================] - 0s 7ms/step - loss: 4.6507e-05\n", 498 | "Epoch 201/500\n", 499 | "1/1 [==============================] - 0s 3ms/step - loss: 4.5966e-05\n", 500 | "Epoch 202/500\n", 501 | "1/1 [==============================] - 0s 2ms/step - loss: 4.5431e-05\n", 502 | "Epoch 203/500\n", 503 | "1/1 [==============================] - 0s 2ms/step - loss: 4.4902e-05\n", 504 | "Epoch 204/500\n", 505 | "1/1 [==============================] - 0s 2ms/step - loss: 4.4380e-05\n", 506 | "Epoch 205/500\n", 507 | "1/1 [==============================] - 0s 2ms/step - loss: 4.3863e-05\n", 508 | "Epoch 206/500\n", 509 | "1/1 [==============================] - 0s 1ms/step - loss: 4.3352e-05\n", 510 | "Epoch 207/500\n", 511 | "1/1 [==============================] - 0s 2ms/step - loss: 4.2848e-05\n", 512 | "Epoch 208/500\n", 513 | "1/1 [==============================] - 0s 1ms/step - loss: 4.2349e-05\n", 514 | "Epoch 209/500\n", 515 | "1/1 [==============================] - 0s 1ms/step - loss: 4.1856e-05\n", 516 | "Epoch 210/500\n", 517 | "1/1 [==============================] - 0s 1ms/step - loss: 4.1370e-05\n", 518 | "Epoch 211/500\n", 519 | "1/1 [==============================] - 0s 1ms/step - loss: 4.0888e-05\n", 520 | "Epoch 212/500\n", 521 | "1/1 [==============================] - 0s 1ms/step - loss: 4.0412e-05\n", 522 | "Epoch 213/500\n", 523 | "1/1 [==============================] - 0s 1ms/step - loss: 3.9942e-05\n", 524 | "Epoch 214/500\n", 525 | "1/1 [==============================] - 0s 1ms/step - loss: 3.9477e-05\n", 526 | "Epoch 215/500\n", 527 | "1/1 [==============================] - 0s 3ms/step - loss: 3.9018e-05\n", 528 | "Epoch 216/500\n", 529 | "1/1 [==============================] - 0s 1ms/step - loss: 3.8563e-05\n", 530 | "Epoch 217/500\n", 531 | "1/1 [==============================] - 0s 3ms/step - loss: 3.8114e-05\n", 532 | "Epoch 218/500\n", 533 | "1/1 [==============================] - 0s 1ms/step - loss: 3.7671e-05\n", 534 | "Epoch 219/500\n", 535 | "1/1 [==============================] - 0s 1ms/step - loss: 3.7233e-05\n", 536 | "Epoch 220/500\n", 537 | "1/1 [==============================] - 0s 1ms/step - loss: 3.6799e-05\n", 538 | "Epoch 221/500\n", 539 | "1/1 [==============================] - 0s 1ms/step - loss: 3.6371e-05\n", 540 | "Epoch 222/500\n", 541 | "1/1 [==============================] - 0s 1ms/step - loss: 3.5948e-05\n", 542 | "Epoch 223/500\n", 543 | "1/1 [==============================] - 0s 5ms/step - loss: 3.5529e-05\n", 544 | "Epoch 224/500\n", 545 | "1/1 [==============================] - 0s 2ms/step - loss: 3.5116e-05\n", 546 | "Epoch 225/500\n", 547 | "1/1 [==============================] - 0s 2ms/step - loss: 3.4707e-05\n", 548 | "Epoch 226/500\n", 549 | "1/1 [==============================] - 0s 1ms/step - loss: 3.4303e-05\n", 550 | "Epoch 227/500\n", 551 | "1/1 [==============================] - 0s 1ms/step - loss: 3.3904e-05\n", 552 | "Epoch 228/500\n", 553 | "1/1 [==============================] - 0s 2ms/step - loss: 3.3509e-05\n", 554 | "Epoch 229/500\n", 555 | "1/1 [==============================] - 0s 2ms/step - loss: 3.3120e-05\n", 556 | "Epoch 230/500\n", 557 | "1/1 [==============================] - 0s 4ms/step - loss: 3.2734e-05\n", 558 | "Epoch 231/500\n", 559 | "1/1 [==============================] - 0s 1ms/step - loss: 3.2353e-05\n", 560 | "Epoch 232/500\n", 561 | "1/1 [==============================] - 0s 1ms/step - loss: 3.1976e-05\n", 562 | "Epoch 233/500\n", 563 | "1/1 [==============================] - 0s 1ms/step - loss: 3.1605e-05\n", 564 | "Epoch 234/500\n", 565 | "1/1 [==============================] - 0s 2ms/step - loss: 3.1237e-05\n", 566 | "Epoch 235/500\n", 567 | "1/1 [==============================] - 0s 1ms/step - loss: 3.0873e-05\n", 568 | "Epoch 236/500\n", 569 | "1/1 [==============================] - 0s 2ms/step - loss: 3.0514e-05\n", 570 | "Epoch 237/500\n", 571 | "1/1 [==============================] - 0s 2ms/step - loss: 3.0159e-05\n", 572 | "Epoch 238/500\n", 573 | "1/1 [==============================] - 0s 2ms/step - loss: 2.9808e-05\n", 574 | "Epoch 239/500\n", 575 | "1/1 [==============================] - 0s 2ms/step - loss: 2.9461e-05\n", 576 | "Epoch 240/500\n", 577 | "1/1 [==============================] - 0s 1ms/step - loss: 2.9118e-05\n", 578 | "Epoch 241/500\n", 579 | "1/1 [==============================] - 0s 2ms/step - loss: 2.8780e-05\n", 580 | "Epoch 242/500\n", 581 | "1/1 [==============================] - 0s 1ms/step - loss: 2.8445e-05\n", 582 | "Epoch 243/500\n", 583 | "1/1 [==============================] - 0s 1ms/step - loss: 2.8113e-05\n", 584 | "Epoch 244/500\n", 585 | "1/1 [==============================] - 0s 1ms/step - loss: 2.7786e-05\n", 586 | "Epoch 245/500\n", 587 | "1/1 [==============================] - 0s 2ms/step - loss: 2.7463e-05\n", 588 | "Epoch 246/500\n", 589 | "1/1 [==============================] - 0s 1ms/step - loss: 2.7143e-05\n", 590 | "Epoch 247/500\n", 591 | "1/1 [==============================] - 0s 1ms/step - loss: 2.6827e-05\n", 592 | "Epoch 248/500\n", 593 | "1/1 [==============================] - 0s 2ms/step - loss: 2.6515e-05\n", 594 | "Epoch 249/500\n", 595 | "1/1 [==============================] - 0s 1ms/step - loss: 2.6207e-05\n", 596 | "Epoch 250/500\n", 597 | "1/1 [==============================] - 0s 2ms/step - loss: 2.5901e-05\n", 598 | "Epoch 251/500\n", 599 | "1/1 [==============================] - 0s 2ms/step - loss: 2.5600e-05\n", 600 | "Epoch 252/500\n", 601 | "1/1 [==============================] - 0s 1ms/step - loss: 2.5302e-05\n", 602 | "Epoch 253/500\n", 603 | "1/1 [==============================] - 0s 1ms/step - loss: 2.5007e-05\n", 604 | "Epoch 254/500\n", 605 | "1/1 [==============================] - 0s 1ms/step - loss: 2.4717e-05\n", 606 | "Epoch 255/500\n", 607 | "1/1 [==============================] - 0s 1ms/step - loss: 2.4429e-05\n", 608 | "Epoch 256/500\n", 609 | "1/1 [==============================] - 0s 2ms/step - loss: 2.4145e-05\n", 610 | "Epoch 257/500\n", 611 | "1/1 [==============================] - 0s 1ms/step - loss: 2.3863e-05\n", 612 | "Epoch 258/500\n", 613 | "1/1 [==============================] - 0s 2ms/step - loss: 2.3586e-05\n", 614 | "Epoch 259/500\n", 615 | "1/1 [==============================] - 0s 1ms/step - loss: 2.3311e-05\n", 616 | "Epoch 260/500\n", 617 | "1/1 [==============================] - 0s 1ms/step - loss: 2.3040e-05\n", 618 | "Epoch 261/500\n", 619 | "1/1 [==============================] - 0s 2ms/step - loss: 2.2772e-05\n", 620 | "Epoch 262/500\n", 621 | "1/1 [==============================] - 0s 1ms/step - loss: 2.2507e-05\n", 622 | "Epoch 263/500\n", 623 | "1/1 [==============================] - 0s 1ms/step - loss: 2.2245e-05\n", 624 | "Epoch 264/500\n", 625 | "1/1 [==============================] - 0s 2ms/step - loss: 2.1986e-05\n", 626 | "Epoch 265/500\n", 627 | "1/1 [==============================] - 0s 2ms/step - loss: 2.1730e-05\n", 628 | "Epoch 266/500\n", 629 | "1/1 [==============================] - 0s 2ms/step - loss: 2.1478e-05\n", 630 | "Epoch 267/500\n", 631 | "1/1 [==============================] - 0s 1ms/step - loss: 2.1228e-05\n", 632 | "Epoch 268/500\n", 633 | "1/1 [==============================] - 0s 2ms/step - loss: 2.0981e-05\n", 634 | "Epoch 269/500\n", 635 | "1/1 [==============================] - 0s 1ms/step - loss: 2.0736e-05\n", 636 | "Epoch 270/500\n", 637 | "1/1 [==============================] - 0s 2ms/step - loss: 2.0495e-05\n", 638 | "Epoch 271/500\n", 639 | "1/1 [==============================] - 0s 1ms/step - loss: 2.0256e-05\n", 640 | "Epoch 272/500\n", 641 | "1/1 [==============================] - 0s 1ms/step - loss: 2.0021e-05\n", 642 | "Epoch 273/500\n", 643 | "1/1 [==============================] - 0s 2ms/step - loss: 1.9788e-05\n", 644 | "Epoch 274/500\n", 645 | "1/1 [==============================] - 0s 3ms/step - loss: 1.9558e-05\n", 646 | "Epoch 275/500\n", 647 | "1/1 [==============================] - 0s 2ms/step - loss: 1.9330e-05\n", 648 | "Epoch 276/500\n", 649 | "1/1 [==============================] - 0s 1ms/step - loss: 1.9105e-05\n", 650 | "Epoch 277/500\n", 651 | "1/1 [==============================] - 0s 1ms/step - loss: 1.8883e-05\n", 652 | "Epoch 278/500\n", 653 | "1/1 [==============================] - 0s 2ms/step - loss: 1.8663e-05\n", 654 | "Epoch 279/500\n", 655 | "1/1 [==============================] - 0s 1ms/step - loss: 1.8446e-05\n", 656 | "Epoch 280/500\n", 657 | "1/1 [==============================] - 0s 1ms/step - loss: 1.8231e-05\n", 658 | "Epoch 281/500\n", 659 | "1/1 [==============================] - 0s 2ms/step - loss: 1.8019e-05\n", 660 | "Epoch 282/500\n", 661 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7809e-05\n", 662 | "Epoch 283/500\n", 663 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7602e-05\n", 664 | "Epoch 284/500\n", 665 | "1/1 [==============================] - 0s 4ms/step - loss: 1.7397e-05\n", 666 | "Epoch 285/500\n", 667 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7195e-05\n", 668 | "Epoch 286/500\n", 669 | "1/1 [==============================] - 0s 1ms/step - loss: 1.6994e-05\n", 670 | "Epoch 287/500\n", 671 | "1/1 [==============================] - 0s 1ms/step - loss: 1.6797e-05\n", 672 | "Epoch 288/500\n", 673 | "1/1 [==============================] - 0s 3ms/step - loss: 1.6601e-05\n", 674 | "Epoch 289/500\n", 675 | "1/1 [==============================] - 0s 1ms/step - loss: 1.6408e-05\n", 676 | "Epoch 290/500\n", 677 | "1/1 [==============================] - 0s 1ms/step - loss: 1.6217e-05\n", 678 | "Epoch 291/500\n", 679 | "1/1 [==============================] - 0s 4ms/step - loss: 1.6028e-05\n", 680 | "Epoch 292/500\n", 681 | "1/1 [==============================] - 0s 1ms/step - loss: 1.5842e-05\n", 682 | "Epoch 293/500\n", 683 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5657e-05\n", 684 | "Epoch 294/500\n", 685 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5475e-05\n", 686 | "Epoch 295/500\n", 687 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5295e-05\n", 688 | "Epoch 296/500\n", 689 | "1/1 [==============================] - 0s 1ms/step - loss: 1.5117e-05\n", 690 | "Epoch 297/500\n", 691 | "1/1 [==============================] - 0s 2ms/step - loss: 1.4941e-05\n", 692 | "Epoch 298/500\n", 693 | "1/1 [==============================] - 0s 1ms/step - loss: 1.4767e-05\n", 694 | "Epoch 299/500\n", 695 | "1/1 [==============================] - 0s 3ms/step - loss: 1.4595e-05\n", 696 | "Epoch 300/500\n", 697 | "1/1 [==============================] - 0s 2ms/step - loss: 1.4425e-05\n", 698 | "Epoch 301/500\n", 699 | "1/1 [==============================] - 0s 4ms/step - loss: 1.4258e-05\n", 700 | "Epoch 302/500\n", 701 | "1/1 [==============================] - 0s 1ms/step - loss: 1.4092e-05\n", 702 | "Epoch 303/500\n", 703 | "1/1 [==============================] - 0s 1ms/step - loss: 1.3928e-05\n", 704 | "Epoch 304/500\n", 705 | "1/1 [==============================] - 0s 4ms/step - loss: 1.3765e-05\n", 706 | "Epoch 305/500\n", 707 | "1/1 [==============================] - 0s 1ms/step - loss: 1.3605e-05\n", 708 | "Epoch 306/500\n", 709 | "1/1 [==============================] - 0s 2ms/step - loss: 1.3447e-05\n", 710 | "Epoch 307/500\n", 711 | "1/1 [==============================] - 0s 5ms/step - loss: 1.3290e-05\n", 712 | "Epoch 308/500\n", 713 | "1/1 [==============================] - 0s 1ms/step - loss: 1.3136e-05\n", 714 | "Epoch 309/500\n", 715 | "1/1 [==============================] - 0s 1ms/step - loss: 1.2983e-05\n", 716 | "Epoch 310/500\n", 717 | "1/1 [==============================] - 0s 1ms/step - loss: 1.2832e-05\n", 718 | "Epoch 311/500\n", 719 | "1/1 [==============================] - 0s 6ms/step - loss: 1.2683e-05\n", 720 | "Epoch 312/500\n", 721 | "1/1 [==============================] - 0s 4ms/step - loss: 1.2535e-05\n", 722 | "Epoch 313/500\n", 723 | "1/1 [==============================] - 0s 2ms/step - loss: 1.2389e-05\n", 724 | "Epoch 314/500\n", 725 | "1/1 [==============================] - 0s 2ms/step - loss: 1.2245e-05\n", 726 | "Epoch 315/500\n", 727 | "1/1 [==============================] - 0s 2ms/step - loss: 1.2103e-05\n", 728 | "Epoch 316/500\n", 729 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1962e-05\n", 730 | "Epoch 317/500\n", 731 | "1/1 [==============================] - 0s 2ms/step - loss: 1.1822e-05\n", 732 | "Epoch 318/500\n", 733 | "1/1 [==============================] - 0s 3ms/step - loss: 1.1685e-05\n", 734 | "Epoch 319/500\n", 735 | "1/1 [==============================] - 0s 1ms/step - loss: 1.1549e-05\n", 736 | "Epoch 320/500\n", 737 | "1/1 [==============================] - 0s 1ms/step - loss: 1.1414e-05\n", 738 | "Epoch 321/500\n", 739 | "1/1 [==============================] - 0s 1ms/step - loss: 1.1282e-05\n", 740 | "Epoch 322/500\n", 741 | "1/1 [==============================] - 0s 1ms/step - loss: 1.1150e-05\n", 742 | "Epoch 323/500\n", 743 | "1/1 [==============================] - 0s 4ms/step - loss: 1.1021e-05\n", 744 | "Epoch 324/500\n", 745 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0892e-05\n", 746 | "Epoch 325/500\n", 747 | "1/1 [==============================] - 0s 1ms/step - loss: 1.0766e-05\n", 748 | "Epoch 326/500\n", 749 | "1/1 [==============================] - 0s 4ms/step - loss: 1.0640e-05\n", 750 | "Epoch 327/500\n", 751 | "1/1 [==============================] - 0s 1ms/step - loss: 1.0517e-05\n", 752 | "Epoch 328/500\n", 753 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0394e-05\n", 754 | "Epoch 329/500\n", 755 | "1/1 [==============================] - 0s 1ms/step - loss: 1.0273e-05\n", 756 | "Epoch 330/500\n", 757 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0153e-05\n", 758 | "Epoch 331/500\n", 759 | "1/1 [==============================] - 0s 2ms/step - loss: 1.0035e-05\n", 760 | "Epoch 332/500\n", 761 | "1/1 [==============================] - 0s 2ms/step - loss: 9.9185e-06\n", 762 | "Epoch 333/500\n", 763 | "1/1 [==============================] - 0s 6ms/step - loss: 9.8030e-06\n", 764 | "Epoch 334/500\n", 765 | "1/1 [==============================] - 0s 2ms/step - loss: 9.6890e-06\n", 766 | "Epoch 335/500\n", 767 | "1/1 [==============================] - 0s 2ms/step - loss: 9.5762e-06\n", 768 | "Epoch 336/500\n", 769 | "1/1 [==============================] - 0s 1ms/step - loss: 9.4647e-06\n", 770 | "Epoch 337/500\n", 771 | "1/1 [==============================] - 0s 2ms/step - loss: 9.3547e-06\n", 772 | "Epoch 338/500\n", 773 | "1/1 [==============================] - 0s 2ms/step - loss: 9.2458e-06\n", 774 | "Epoch 339/500\n", 775 | "1/1 [==============================] - 0s 1ms/step - loss: 9.1382e-06\n", 776 | "Epoch 340/500\n", 777 | "1/1 [==============================] - 0s 2ms/step - loss: 9.0318e-06\n", 778 | "Epoch 341/500\n", 779 | "1/1 [==============================] - 0s 4ms/step - loss: 8.9267e-06\n", 780 | "Epoch 342/500\n", 781 | "1/1 [==============================] - 0s 1ms/step - loss: 8.8229e-06\n", 782 | "Epoch 343/500\n", 783 | "1/1 [==============================] - 0s 1ms/step - loss: 8.7201e-06\n", 784 | "Epoch 344/500\n", 785 | "1/1 [==============================] - 0s 4ms/step - loss: 8.6188e-06\n", 786 | "Epoch 345/500\n", 787 | "1/1 [==============================] - 0s 2ms/step - loss: 8.5183e-06\n", 788 | "Epoch 346/500\n", 789 | "1/1 [==============================] - 0s 3ms/step - loss: 8.4192e-06\n", 790 | "Epoch 347/500\n", 791 | "1/1 [==============================] - 0s 2ms/step - loss: 8.3212e-06\n", 792 | "Epoch 348/500\n", 793 | "1/1 [==============================] - 0s 2ms/step - loss: 8.2243e-06\n", 794 | "Epoch 349/500\n", 795 | "1/1 [==============================] - 0s 3ms/step - loss: 8.1287e-06\n", 796 | "Epoch 350/500\n", 797 | "1/1 [==============================] - 0s 3ms/step - loss: 8.0340e-06\n", 798 | "Epoch 351/500\n", 799 | "1/1 [==============================] - 0s 2ms/step - loss: 7.9405e-06\n", 800 | "Epoch 352/500\n", 801 | "1/1 [==============================] - 0s 2ms/step - loss: 7.8481e-06\n", 802 | "Epoch 353/500\n", 803 | "1/1 [==============================] - 0s 5ms/step - loss: 7.7567e-06\n", 804 | "Epoch 354/500\n", 805 | "1/1 [==============================] - 0s 1ms/step - loss: 7.6666e-06\n", 806 | "Epoch 355/500\n", 807 | "1/1 [==============================] - 0s 1ms/step - loss: 7.5772e-06\n", 808 | "Epoch 356/500\n", 809 | "1/1 [==============================] - 0s 1ms/step - loss: 7.4892e-06\n", 810 | "Epoch 357/500\n", 811 | "1/1 [==============================] - 0s 2ms/step - loss: 7.4020e-06\n", 812 | "Epoch 358/500\n", 813 | "1/1 [==============================] - 0s 1ms/step - loss: 7.3158e-06\n", 814 | "Epoch 359/500\n", 815 | "1/1 [==============================] - 0s 1ms/step - loss: 7.2307e-06\n", 816 | "Epoch 360/500\n", 817 | "1/1 [==============================] - 0s 3ms/step - loss: 7.1466e-06\n", 818 | "Epoch 361/500\n", 819 | "1/1 [==============================] - 0s 2ms/step - loss: 7.0634e-06\n", 820 | "Epoch 362/500\n", 821 | "1/1 [==============================] - 0s 1ms/step - loss: 6.9811e-06\n", 822 | "Epoch 363/500\n", 823 | "1/1 [==============================] - 0s 2ms/step - loss: 6.8999e-06\n", 824 | "Epoch 364/500\n", 825 | "1/1 [==============================] - 0s 1ms/step - loss: 6.8196e-06\n", 826 | "Epoch 365/500\n", 827 | "1/1 [==============================] - 0s 1ms/step - loss: 6.7403e-06\n", 828 | "Epoch 366/500\n", 829 | "1/1 [==============================] - 0s 2ms/step - loss: 6.6618e-06\n", 830 | "Epoch 367/500\n", 831 | "1/1 [==============================] - 0s 1ms/step - loss: 6.5842e-06\n", 832 | "Epoch 368/500\n", 833 | "1/1 [==============================] - 0s 2ms/step - loss: 6.5077e-06\n", 834 | "Epoch 369/500\n", 835 | "1/1 [==============================] - 0s 2ms/step - loss: 6.4319e-06\n", 836 | "Epoch 370/500\n", 837 | "1/1 [==============================] - 0s 1ms/step - loss: 6.3572e-06\n", 838 | "Epoch 371/500\n", 839 | "1/1 [==============================] - 0s 1ms/step - loss: 6.2832e-06\n", 840 | "Epoch 372/500\n", 841 | "1/1 [==============================] - 0s 1ms/step - loss: 6.2100e-06\n", 842 | "Epoch 373/500\n", 843 | "1/1 [==============================] - 0s 1ms/step - loss: 6.1377e-06\n", 844 | "Epoch 374/500\n", 845 | "1/1 [==============================] - 0s 1ms/step - loss: 6.0662e-06\n", 846 | "Epoch 375/500\n", 847 | "1/1 [==============================] - 0s 2ms/step - loss: 5.9958e-06\n", 848 | "Epoch 376/500\n", 849 | "1/1 [==============================] - 0s 1ms/step - loss: 5.9259e-06\n", 850 | "Epoch 377/500\n", 851 | "1/1 [==============================] - 0s 1ms/step - loss: 5.8569e-06\n", 852 | "Epoch 378/500\n", 853 | "1/1 [==============================] - 0s 2ms/step - loss: 5.7888e-06\n", 854 | "Epoch 379/500\n", 855 | "1/1 [==============================] - 0s 2ms/step - loss: 5.7214e-06\n", 856 | "Epoch 380/500\n", 857 | "1/1 [==============================] - 0s 3ms/step - loss: 5.6549e-06\n", 858 | "Epoch 381/500\n", 859 | "1/1 [==============================] - 0s 2ms/step - loss: 5.5892e-06\n", 860 | "Epoch 382/500\n", 861 | "1/1 [==============================] - 0s 2ms/step - loss: 5.5240e-06\n", 862 | "Epoch 383/500\n", 863 | "1/1 [==============================] - 0s 1ms/step - loss: 5.4597e-06\n", 864 | "Epoch 384/500\n", 865 | "1/1 [==============================] - 0s 1ms/step - loss: 5.3963e-06\n", 866 | "Epoch 385/500\n", 867 | "1/1 [==============================] - 0s 2ms/step - loss: 5.3333e-06\n", 868 | "Epoch 386/500\n", 869 | "1/1 [==============================] - 0s 2ms/step - loss: 5.2713e-06\n", 870 | "Epoch 387/500\n", 871 | "1/1 [==============================] - 0s 2ms/step - loss: 5.2099e-06\n", 872 | "Epoch 388/500\n", 873 | "1/1 [==============================] - 0s 1ms/step - loss: 5.1493e-06\n", 874 | "Epoch 389/500\n", 875 | "1/1 [==============================] - 0s 1ms/step - loss: 5.0893e-06\n", 876 | "Epoch 390/500\n", 877 | "1/1 [==============================] - 0s 2ms/step - loss: 5.0300e-06\n", 878 | "Epoch 391/500\n", 879 | "1/1 [==============================] - 0s 2ms/step - loss: 4.9716e-06\n", 880 | "Epoch 392/500\n", 881 | "1/1 [==============================] - 0s 1ms/step - loss: 4.9138e-06\n", 882 | "Epoch 393/500\n", 883 | "1/1 [==============================] - 0s 1ms/step - loss: 4.8565e-06\n", 884 | "Epoch 394/500\n", 885 | "1/1 [==============================] - 0s 1ms/step - loss: 4.8000e-06\n", 886 | "Epoch 395/500\n", 887 | "1/1 [==============================] - 0s 3ms/step - loss: 4.7442e-06\n", 888 | "Epoch 396/500\n", 889 | "1/1 [==============================] - 0s 2ms/step - loss: 4.6891e-06\n", 890 | "Epoch 397/500\n", 891 | "1/1 [==============================] - 0s 2ms/step - loss: 4.6346e-06\n", 892 | "Epoch 398/500\n", 893 | "1/1 [==============================] - 0s 1ms/step - loss: 4.5805e-06\n", 894 | "Epoch 399/500\n", 895 | "1/1 [==============================] - 0s 1ms/step - loss: 4.5273e-06\n", 896 | "Epoch 400/500\n", 897 | "1/1 [==============================] - 0s 1ms/step - loss: 4.4746e-06\n", 898 | "Epoch 401/500\n", 899 | "1/1 [==============================] - 0s 2ms/step - loss: 4.4225e-06\n", 900 | "Epoch 402/500\n", 901 | "1/1 [==============================] - 0s 2ms/step - loss: 4.3711e-06\n", 902 | "Epoch 403/500\n", 903 | "1/1 [==============================] - 0s 2ms/step - loss: 4.3201e-06\n", 904 | "Epoch 404/500\n", 905 | "1/1 [==============================] - 0s 1ms/step - loss: 4.2700e-06\n", 906 | "Epoch 405/500\n", 907 | "1/1 [==============================] - 0s 3ms/step - loss: 4.2202e-06\n", 908 | "Epoch 406/500\n", 909 | "1/1 [==============================] - 0s 1ms/step - loss: 4.1710e-06\n", 910 | "Epoch 407/500\n", 911 | "1/1 [==============================] - 0s 2ms/step - loss: 4.1224e-06\n", 912 | "Epoch 408/500\n", 913 | "1/1 [==============================] - 0s 3ms/step - loss: 4.0745e-06\n", 914 | "Epoch 409/500\n", 915 | "1/1 [==============================] - 0s 3ms/step - loss: 4.0271e-06\n", 916 | "Epoch 410/500\n", 917 | "1/1 [==============================] - 0s 2ms/step - loss: 3.9803e-06\n", 918 | "Epoch 411/500\n", 919 | "1/1 [==============================] - 0s 2ms/step - loss: 3.9340e-06\n", 920 | "Epoch 412/500\n", 921 | "1/1 [==============================] - 0s 1ms/step - loss: 3.8882e-06\n", 922 | "Epoch 413/500\n", 923 | "1/1 [==============================] - 0s 2ms/step - loss: 3.8429e-06\n", 924 | "Epoch 414/500\n", 925 | "1/1 [==============================] - 0s 2ms/step - loss: 3.7981e-06\n", 926 | "Epoch 415/500\n", 927 | "1/1 [==============================] - 0s 2ms/step - loss: 3.7539e-06\n", 928 | "Epoch 416/500\n", 929 | "1/1 [==============================] - 0s 1ms/step - loss: 3.7102e-06\n", 930 | "Epoch 417/500\n", 931 | "1/1 [==============================] - 0s 2ms/step - loss: 3.6670e-06\n", 932 | "Epoch 418/500\n", 933 | "1/1 [==============================] - 0s 1ms/step - loss: 3.6243e-06\n", 934 | "Epoch 419/500\n", 935 | "1/1 [==============================] - 0s 2ms/step - loss: 3.5821e-06\n", 936 | "Epoch 420/500\n", 937 | "1/1 [==============================] - 0s 2ms/step - loss: 3.5403e-06\n", 938 | "Epoch 421/500\n", 939 | "1/1 [==============================] - 0s 1ms/step - loss: 3.4992e-06\n", 940 | "Epoch 422/500\n", 941 | "1/1 [==============================] - 0s 5ms/step - loss: 3.4585e-06\n", 942 | "Epoch 423/500\n", 943 | "1/1 [==============================] - 0s 1ms/step - loss: 3.4182e-06\n", 944 | "Epoch 424/500\n", 945 | "1/1 [==============================] - 0s 1ms/step - loss: 3.3784e-06\n", 946 | "Epoch 425/500\n", 947 | "1/1 [==============================] - 0s 2ms/step - loss: 3.3391e-06\n", 948 | "Epoch 426/500\n", 949 | "1/1 [==============================] - 0s 2ms/step - loss: 3.3003e-06\n", 950 | "Epoch 427/500\n", 951 | "1/1 [==============================] - 0s 2ms/step - loss: 3.2619e-06\n", 952 | "Epoch 428/500\n", 953 | "1/1 [==============================] - 0s 2ms/step - loss: 3.2238e-06\n", 954 | "Epoch 429/500\n", 955 | "1/1 [==============================] - 0s 1ms/step - loss: 3.1864e-06\n", 956 | "Epoch 430/500\n", 957 | "1/1 [==============================] - 0s 1ms/step - loss: 3.1492e-06\n", 958 | "Epoch 431/500\n", 959 | "1/1 [==============================] - 0s 4ms/step - loss: 3.1127e-06\n", 960 | "Epoch 432/500\n", 961 | "1/1 [==============================] - 0s 2ms/step - loss: 3.0763e-06\n", 962 | "Epoch 433/500\n", 963 | "1/1 [==============================] - 0s 2ms/step - loss: 3.0406e-06\n", 964 | "Epoch 434/500\n", 965 | "1/1 [==============================] - 0s 2ms/step - loss: 3.0053e-06\n", 966 | "Epoch 435/500\n", 967 | "1/1 [==============================] - 0s 1ms/step - loss: 2.9703e-06\n", 968 | "Epoch 436/500\n", 969 | "1/1 [==============================] - 0s 1ms/step - loss: 2.9357e-06\n", 970 | "Epoch 437/500\n", 971 | "1/1 [==============================] - 0s 1ms/step - loss: 2.9015e-06\n", 972 | "Epoch 438/500\n", 973 | "1/1 [==============================] - 0s 2ms/step - loss: 2.8677e-06\n", 974 | "Epoch 439/500\n", 975 | "1/1 [==============================] - 0s 2ms/step - loss: 2.8344e-06\n", 976 | "Epoch 440/500\n", 977 | "1/1 [==============================] - 0s 2ms/step - loss: 2.8014e-06\n", 978 | "Epoch 441/500\n", 979 | "1/1 [==============================] - 0s 2ms/step - loss: 2.7688e-06\n", 980 | "Epoch 442/500\n", 981 | "1/1 [==============================] - 0s 5ms/step - loss: 2.7365e-06\n", 982 | "Epoch 443/500\n", 983 | "1/1 [==============================] - 0s 3ms/step - loss: 2.7046e-06\n", 984 | "Epoch 444/500\n", 985 | "1/1 [==============================] - 0s 1ms/step - loss: 2.6731e-06\n", 986 | "Epoch 445/500\n", 987 | "1/1 [==============================] - 0s 1ms/step - loss: 2.6421e-06\n", 988 | "Epoch 446/500\n", 989 | "1/1 [==============================] - 0s 2ms/step - loss: 2.6113e-06\n", 990 | "Epoch 447/500\n", 991 | "1/1 [==============================] - 0s 5ms/step - loss: 2.5810e-06\n", 992 | "Epoch 448/500\n", 993 | "1/1 [==============================] - 0s 1ms/step - loss: 2.5509e-06\n", 994 | "Epoch 449/500\n", 995 | "1/1 [==============================] - 0s 1ms/step - loss: 2.5212e-06\n", 996 | "Epoch 450/500\n", 997 | "1/1 [==============================] - 0s 1ms/step - loss: 2.4919e-06\n", 998 | "Epoch 451/500\n", 999 | "1/1 [==============================] - 0s 1ms/step - loss: 2.4629e-06\n", 1000 | "Epoch 452/500\n", 1001 | "1/1 [==============================] - 0s 1ms/step - loss: 2.4343e-06\n", 1002 | "Epoch 453/500\n", 1003 | "1/1 [==============================] - 0s 2ms/step - loss: 2.4059e-06\n", 1004 | "Epoch 454/500\n", 1005 | "1/1 [==============================] - 0s 3ms/step - loss: 2.3779e-06\n", 1006 | "Epoch 455/500\n", 1007 | "1/1 [==============================] - 0s 1ms/step - loss: 2.3502e-06\n", 1008 | "Epoch 456/500\n", 1009 | "1/1 [==============================] - 0s 2ms/step - loss: 2.3229e-06\n", 1010 | "Epoch 457/500\n", 1011 | "1/1 [==============================] - 0s 2ms/step - loss: 2.2958e-06\n", 1012 | "Epoch 458/500\n", 1013 | "1/1 [==============================] - 0s 1ms/step - loss: 2.2692e-06\n", 1014 | "Epoch 459/500\n", 1015 | "1/1 [==============================] - 0s 1ms/step - loss: 2.2427e-06\n", 1016 | "Epoch 460/500\n", 1017 | "1/1 [==============================] - 0s 1ms/step - loss: 2.2166e-06\n", 1018 | "Epoch 461/500\n", 1019 | "1/1 [==============================] - 0s 5ms/step - loss: 2.1909e-06\n", 1020 | "Epoch 462/500\n", 1021 | "1/1 [==============================] - 0s 1ms/step - loss: 2.1653e-06\n", 1022 | "Epoch 463/500\n", 1023 | "1/1 [==============================] - 0s 1ms/step - loss: 2.1402e-06\n", 1024 | "Epoch 464/500\n", 1025 | "1/1 [==============================] - 0s 2ms/step - loss: 2.1152e-06\n", 1026 | "Epoch 465/500\n", 1027 | "1/1 [==============================] - 0s 2ms/step - loss: 2.0906e-06\n", 1028 | "Epoch 466/500\n", 1029 | "1/1 [==============================] - 0s 4ms/step - loss: 2.0664e-06\n", 1030 | "Epoch 467/500\n", 1031 | "1/1 [==============================] - 0s 2ms/step - loss: 2.0423e-06\n", 1032 | "Epoch 468/500\n", 1033 | "1/1 [==============================] - 0s 2ms/step - loss: 2.0185e-06\n", 1034 | "Epoch 469/500\n", 1035 | "1/1 [==============================] - 0s 4ms/step - loss: 1.9950e-06\n", 1036 | "Epoch 470/500\n", 1037 | "1/1 [==============================] - 0s 2ms/step - loss: 1.9718e-06\n", 1038 | "Epoch 471/500\n", 1039 | "1/1 [==============================] - 0s 1ms/step - loss: 1.9489e-06\n", 1040 | "Epoch 472/500\n", 1041 | "1/1 [==============================] - 0s 2ms/step - loss: 1.9262e-06\n", 1042 | "Epoch 473/500\n", 1043 | "1/1 [==============================] - 0s 2ms/step - loss: 1.9038e-06\n", 1044 | "Epoch 474/500\n", 1045 | "1/1 [==============================] - 0s 2ms/step - loss: 1.8816e-06\n", 1046 | "Epoch 475/500\n", 1047 | "1/1 [==============================] - 0s 2ms/step - loss: 1.8597e-06\n", 1048 | "Epoch 476/500\n", 1049 | "1/1 [==============================] - 0s 1ms/step - loss: 1.8381e-06\n", 1050 | "Epoch 477/500\n", 1051 | "1/1 [==============================] - 0s 1ms/step - loss: 1.8166e-06\n", 1052 | "Epoch 478/500\n", 1053 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7955e-06\n", 1054 | "Epoch 479/500\n", 1055 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7746e-06\n", 1056 | "Epoch 480/500\n", 1057 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7540e-06\n", 1058 | "Epoch 481/500\n", 1059 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7336e-06\n", 1060 | "Epoch 482/500\n", 1061 | "1/1 [==============================] - 0s 2ms/step - loss: 1.7135e-06\n", 1062 | "Epoch 483/500\n", 1063 | "1/1 [==============================] - 0s 1ms/step - loss: 1.6935e-06\n", 1064 | "Epoch 484/500\n", 1065 | "1/1 [==============================] - 0s 1ms/step - loss: 1.6738e-06\n", 1066 | "Epoch 485/500\n", 1067 | "1/1 [==============================] - 0s 2ms/step - loss: 1.6543e-06\n", 1068 | "Epoch 486/500\n", 1069 | "1/1 [==============================] - 0s 3ms/step - loss: 1.6351e-06\n", 1070 | "Epoch 487/500\n", 1071 | "1/1 [==============================] - 0s 2ms/step - loss: 1.6160e-06\n", 1072 | "Epoch 488/500\n", 1073 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5972e-06\n", 1074 | "Epoch 489/500\n", 1075 | "1/1 [==============================] - 0s 1ms/step - loss: 1.5787e-06\n", 1076 | "Epoch 490/500\n", 1077 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5603e-06\n", 1078 | "Epoch 491/500\n", 1079 | "1/1 [==============================] - 0s 1ms/step - loss: 1.5422e-06\n", 1080 | "Epoch 492/500\n", 1081 | "1/1 [==============================] - 0s 2ms/step - loss: 1.5241e-06\n", 1082 | "Epoch 493/500\n", 1083 | "1/1 [==============================] - 0s 1ms/step - loss: 1.5064e-06\n", 1084 | "Epoch 494/500\n", 1085 | "1/1 [==============================] - 0s 2ms/step - loss: 1.4889e-06\n", 1086 | "Epoch 495/500\n", 1087 | "1/1 [==============================] - 0s 1ms/step - loss: 1.4717e-06\n", 1088 | "Epoch 496/500\n", 1089 | "1/1 [==============================] - 0s 1ms/step - loss: 1.4545e-06\n", 1090 | "Epoch 497/500\n", 1091 | "1/1 [==============================] - 0s 1ms/step - loss: 1.4375e-06\n", 1092 | "Epoch 498/500\n", 1093 | "1/1 [==============================] - 0s 2ms/step - loss: 1.4208e-06\n", 1094 | "Epoch 499/500\n", 1095 | "1/1 [==============================] - 0s 1ms/step - loss: 1.4043e-06\n", 1096 | "Epoch 500/500\n", 1097 | "1/1 [==============================] - 0s 1ms/step - loss: 1.3879e-06\n", 1098 | "4.0 hundred thousand dollars\n" 1099 | ], 1100 | "name": "stdout" 1101 | } 1102 | ] 1103 | } 1104 | ] 1105 | } -------------------------------------------------------------------------------- /Polynomial_Regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Polynomial Regression.ipynb", 7 | "provenance": [], 8 | "authorship_tag": "ABX9TyNGTrlRJzqwcqpYimxSqJ3R", 9 | "include_colab_link": true 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | }, 15 | "language_info": { 16 | "name": "python" 17 | } 18 | }, 19 | "cells": [ 20 | { 21 | "cell_type": "markdown", 22 | "metadata": { 23 | "id": "view-in-github", 24 | "colab_type": "text" 25 | }, 26 | "source": [ 27 | "\"Open" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "source": [ 33 | "import numpy as np\n", 34 | "import matplotlib.pyplot as plt\n", 35 | "import pandas as pd" 36 | ], 37 | "metadata": { 38 | "id": "5A-dcPMK47q7" 39 | }, 40 | "execution_count": null, 41 | "outputs": [] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": null, 46 | "metadata": { 47 | "colab": { 48 | "base_uri": "https://localhost:8080/" 49 | }, 50 | "id": "rvaFKDZg4ot6", 51 | "outputId": "ca58a84a-99f4-4281-96d1-a0b7a1b1eaf3" 52 | }, 53 | "outputs": [ 54 | { 55 | "output_type": "stream", 56 | "name": "stdout", 57 | "text": [ 58 | " timestamp company level ... Race_Hispanic Race Education\n", 59 | "0 6/7/2017 11:33:27 Oracle L3 ... 0 NaN NaN\n", 60 | "1 6/10/2017 17:11:29 eBay SE 2 ... 0 NaN NaN\n", 61 | "2 6/11/2017 14:53:57 Amazon L7 ... 0 NaN NaN\n", 62 | "3 6/17/2017 0:23:14 Apple M1 ... 0 NaN NaN\n", 63 | "4 6/20/2017 10:58:51 Microsoft 60 ... 0 NaN NaN\n", 64 | "... ... ... ... ... ... ... ...\n", 65 | "62637 9/9/2018 11:52:32 Google T4 ... 0 NaN NaN\n", 66 | "62638 9/13/2018 8:23:32 Microsoft 62 ... 0 NaN NaN\n", 67 | "62639 9/13/2018 14:35:59 MSFT 63 ... 0 NaN NaN\n", 68 | "62640 9/16/2018 16:10:35 Salesforce Lead MTS ... 0 NaN NaN\n", 69 | "62641 1/29/2019 5:12:59 apple ict3 ... 0 NaN NaN\n", 70 | "\n", 71 | "[62642 rows x 29 columns]\n" 72 | ] 73 | } 74 | ], 75 | "source": [ 76 | "df = pd.read_csv('/content/Levels_Fyi_Salary_Data.csv')\n", 77 | "print(df)" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "source": [ 83 | "print(df.columns)" 84 | ], 85 | "metadata": { 86 | "colab": { 87 | "base_uri": "https://localhost:8080/" 88 | }, 89 | "id": "lK6kNXvP8P4h", 90 | "outputId": "e70c08a5-e3ed-4f28-a6a0-fa4f6c359225" 91 | }, 92 | "execution_count": null, 93 | "outputs": [ 94 | { 95 | "output_type": "stream", 96 | "name": "stdout", 97 | "text": [ 98 | "Index(['timestamp', 'company', 'level', 'title', 'totalyearlycompensation',\n", 99 | " 'location', 'yearsofexperience', 'yearsatcompany', 'tag', 'basesalary',\n", 100 | " 'stockgrantvalue', 'bonus', 'gender', 'otherdetails', 'cityid', 'dmaid',\n", 101 | " 'rowNumber', 'Masters_Degree', 'Bachelors_Degree', 'Doctorate_Degree',\n", 102 | " 'Highschool', 'Some_College', 'Race_Asian', 'Race_White',\n", 103 | " 'Race_Two_Or_More', 'Race_Black', 'Race_Hispanic', 'Race', 'Education'],\n", 104 | " dtype='object')\n" 105 | ] 106 | } 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "source": [ 112 | "salaryDF = df[['company', 'totalyearlycompensation', 'yearsofexperience', 'yearsatcompany', 'basesalary']]" 113 | ], 114 | "metadata": { 115 | "id": "ygNTMMUT73zV" 116 | }, 117 | "execution_count": null, 118 | "outputs": [] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "source": [ 123 | "print(salaryDF.head())" 124 | ], 125 | "metadata": { 126 | "colab": { 127 | "base_uri": "https://localhost:8080/" 128 | }, 129 | "id": "mtj59MkV8Tbu", 130 | "outputId": "1b270b57-74ea-47fd-ee98-bb268a493fc8" 131 | }, 132 | "execution_count": null, 133 | "outputs": [ 134 | { 135 | "output_type": "stream", 136 | "name": "stdout", 137 | "text": [ 138 | " company totalyearlycompensation ... yearsatcompany basesalary\n", 139 | "0 Oracle 127000 ... 1.5 107000.0\n", 140 | "1 eBay 100000 ... 3.0 0.0\n", 141 | "2 Amazon 310000 ... 0.0 155000.0\n", 142 | "3 Apple 372000 ... 5.0 157000.0\n", 143 | "4 Microsoft 157000 ... 3.0 0.0\n", 144 | "\n", 145 | "[5 rows x 5 columns]\n" 146 | ] 147 | } 148 | ] 149 | }, 150 | { 151 | "cell_type": "markdown", 152 | "source": [ 153 | "Splitting" 154 | ], 155 | "metadata": { 156 | "id": "3NlOa2MgDy4D" 157 | } 158 | }, 159 | { 160 | "cell_type": "code", 161 | "source": [ 162 | "X = salaryDF.iloc[:,2:]\n", 163 | "y_train = salaryDF.iloc[:50000,1]\n", 164 | "y_test = salaryDF.iloc[50000:,1]\n", 165 | "\n", 166 | "rows = X.shape[0]\n", 167 | "count = 0\n", 168 | "N_TRAIN = 50000\n", 169 | "\n", 170 | "train_err = {}\n", 171 | "test_err = {}\n" 172 | ], 173 | "metadata": { 174 | "colab": { 175 | "base_uri": "https://localhost:8080/" 176 | }, 177 | "id": "asIlw040DyR5", 178 | "outputId": "6695e6a1-5dab-4596-ef99-4a12f1ad2249" 179 | }, 180 | "execution_count": null, 181 | "outputs": [ 182 | { 183 | "output_type": "stream", 184 | "name": "stdout", 185 | "text": [ 186 | " yearsofexperience yearsatcompany basesalary\n", 187 | "0 1.5 1.5 107000.0\n", 188 | "1 5.0 3.0 0.0\n", 189 | "2 8.0 0.0 155000.0\n", 190 | "3 7.0 5.0 157000.0\n", 191 | "4 5.0 3.0 0.0\n", 192 | "0 2.25\n", 193 | "1 25.00\n", 194 | "2 64.00\n", 195 | "3 49.00\n", 196 | "4 25.00\n", 197 | " ... \n", 198 | "62637 100.00\n", 199 | "62638 4.00\n", 200 | "62639 196.00\n", 201 | "62640 64.00\n", 202 | "62641 0.00\n", 203 | "Name: yearsofexperience, Length: 62642, dtype: float64\n" 204 | ] 205 | } 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "source": [ 211 | "def rmse(targets, predictions):\n", 212 | " return np.sqrt((np.square(predictions - targets)).mean())" 213 | ], 214 | "metadata": { 215 | "id": "p4nWmuvKUKeM" 216 | }, 217 | "execution_count": null, 218 | "outputs": [] 219 | }, 220 | { 221 | "cell_type": "code", 222 | "source": [ 223 | "for i in range(1,6):\n", 224 | " A = np.ones([rows, (3*i)+1])\n", 225 | " print(A.shape)\n", 226 | " for j in range(0,3):\n", 227 | " for k in range(1, i+1):\n", 228 | " A[:,count]=np.power(np.array(X.iloc[:,j]).T, k)\n", 229 | " count = count+1\n", 230 | " #splitting the x-values with monomials into training and testing\n", 231 | " print(A[1])\n", 232 | " x_train = A[0:N_TRAIN,:]\n", 233 | " x_test = A[N_TRAIN:,:]\n", 234 | " \n", 235 | " print(x_train.shape)\n", 236 | " w = np.linalg.inv(x_train.T.dot(x_train)).dot(x_train.T.dot(y_train))\n", 237 | " #calculate training error\n", 238 | " y_train_pred = x_train.dot(w)\n", 239 | " rms_train = rmse(y_train, y_train_pred)\n", 240 | " train_err[i] = rms_train\n", 241 | " print(rms_train)\n", 242 | " \n", 243 | " \n", 244 | " #calculate test error\n", 245 | " y_test_pred = x_test.dot(w)\n", 246 | " rms_test = rmse(y_test, y_test_pred)\n", 247 | " print(rms_test)\n", 248 | " test_err[i] = rms_test\n", 249 | " \n", 250 | " count = 0" 251 | ], 252 | "metadata": { 253 | "colab": { 254 | "base_uri": "https://localhost:8080/" 255 | }, 256 | "id": "mhYuWCGqL2f6", 257 | "outputId": "fd086fc4-f7cd-4408-b22c-f412b6e0b21b" 258 | }, 259 | "execution_count": null, 260 | "outputs": [ 261 | { 262 | "output_type": "stream", 263 | "name": "stdout", 264 | "text": [ 265 | "(62642, 4)\n", 266 | "[5. 3. 0. 1.]\n", 267 | "(50000, 4)\n", 268 | "92469.43809773953\n", 269 | "121382.52351504724\n", 270 | "(62642, 7)\n", 271 | "[ 5. 25. 3. 9. 0. 0. 1.]\n", 272 | "(50000, 7)\n", 273 | "91573.99026037076\n", 274 | "129644.37305767808\n", 275 | "(62642, 10)\n", 276 | "[ 5. 25. 125. 3. 9. 27. 0. 0. 0. 1.]\n", 277 | "(50000, 10)\n", 278 | "82679.30803704054\n", 279 | "450246.99729011103\n", 280 | "(62642, 13)\n", 281 | "[ 5. 25. 125. 625. 3. 9. 27. 81. 0. 0. 0. 0. 1.]\n", 282 | "(50000, 13)\n", 283 | "76200.18255097712\n", 284 | "1486057.6706302396\n", 285 | "(62642, 16)\n", 286 | "[5.000e+00 2.500e+01 1.250e+02 6.250e+02 3.125e+03 3.000e+00 9.000e+00\n", 287 | " 2.700e+01 8.100e+01 2.430e+02 0.000e+00 0.000e+00 0.000e+00 0.000e+00\n", 288 | " 0.000e+00 1.000e+00]\n", 289 | "(50000, 16)\n", 290 | "75412.29147140722\n", 291 | "1535500.4795176922\n" 292 | ] 293 | } 294 | ] 295 | }, 296 | { 297 | "cell_type": "code", 298 | "source": [ 299 | "# Produce a plot of results.\n", 300 | "plt.plot(list(train_err.keys()), list(train_err.values()))\n", 301 | "plt.plot(list(test_err.keys()), list(test_err.values()))\n", 302 | "plt.ylabel('RMS')\n", 303 | "plt.legend(['Test error','Training error'])\n", 304 | "plt.title('Fit with polynomials, no regularization')\n", 305 | "plt.xlabel('Polynomial degree')\n", 306 | "plt.show()" 307 | ], 308 | "metadata": { 309 | "colab": { 310 | "base_uri": "https://localhost:8080/", 311 | "height": 295 312 | }, 313 | "id": "5Ju_9F9KQFVn", 314 | "outputId": "0e2c1327-09d7-4e77-cada-8ce38de20fd9" 315 | }, 316 | "execution_count": null, 317 | "outputs": [ 318 | { 319 | "output_type": "display_data", 320 | "data": { 321 | "image/png": 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\n", 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" 324 | ] 325 | }, 326 | "metadata": { 327 | "needs_background": "light" 328 | } 329 | } 330 | ] 331 | } 332 | ] 333 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MachineLearningFundamentals -------------------------------------------------------------------------------- /Salary_Data.csv: -------------------------------------------------------------------------------- 1 | YearsExperience,Salary 1.1,39343 1.3,46205 1.5,37731 1.8,49430 2,43525 2.2,39891 2.9,56642 3,60150 3.2,54445 3.2,64445 3.7,57189 3.9,63218 4,55794 4,56957 4.1,57081 4.5,61111 4.9,67938 5.1,66029 5.3,83088 5.9,81363 6,93940 6.5,96540 6.8,91738 7.1,98273 7.9,101302 8.2,113812 8.7,109431 9,105582 9.5,116969 9.6,112635 10.3,122391 10.5,121872 --------------------------------------------------------------------------------